Does the conclusion that Divisia M4 outperforms the federal funds rate depend on sample, price index, or aggregate choice?

No. Chen and Valcarcel (2025) verify the result across three samples (1967–2020, 1988–2020, 2008–2020), two price indexes (CPI and PCE), and two Divisia aggregates (M2 and M4). The Wu-Xia shadow rate produces 72–99% output puzzles and 93–99% price puzzles across all 12 combinations; Divisia M4 produces 2–24% output puzzles and 2–7% price puzzles (with one ambiguous cell in the historical PCE sample where both indicators struggle). The pattern is consistent with Keating et al. (2019) on pre/post-GFC stability and with Chen and Valcarcel (2024) on the stability of Divisia money demand.

Monetary Policy | Robin Chen

Decomposing supply and demand driven inflation in Mexico: Evidence from sectoral analysis

Mon, 13 Apr 2026 00:00:00 +0000

Why Mexican Inflation Behaves Differently: Food Dominates, Services Persist, Housing Barely Moves

Mexican inflation does not follow the developed-economy playbook. Colunga-Ramos, Chen, and Perales (2026, Economics Letters) decompose headline inflation across 31 CPI sectors from 2006 to 2024 and find that food drives both supply and demand swings, services act as a persistent demand floor that explains slow disinflation since 2023, and housing — despite 18% of the CPI basket — contributes almost nothing because prices there barely move. Structural VAR analysis confirms the decomposition captures distinct mechanisms: demand inflation responds to domestic monetary expansions while supply inflation reacts to global supply chain shocks.

Key Concepts

Services floor: The persistent, low-volatility demand-driven contribution of Mexican services — roughly 24% of demand inflation on average but with low correlation to aggregate swings — that prevents disinflation from proceeding as quickly as falling goods prices would suggest. Introduced in Colunga-Ramos, Chen, and Perales (2026) .
Food-dominance pattern: The empirical regularity in Mexico — distinct from the U.S. and euro area — by which food ranks highest in importance for both demand-driven and supply-driven inflation. Reflects large CPI weight, high correlation with aggregate inflation, and Mexico’s exposure to both global commodity cycles and domestic food-demand pressures. Introduced in Colunga-Ramos, Chen, and Perales (2026) .
Housing non-response: The near-zero contribution of Mexican housing to either inflation type, despite housing representing 18.05% of the CPI basket. Implies the housing-wealth and mortgage channels of monetary policy operating in advanced economies (Bernanke and Gertler, 1995 ) work weakly in Mexico.

Where Mexican Inflation Differs from the United States

Category	CPI weight (MX)	Demand importance (MX)	Supply importance (MX)	Role in the U.S. benchmark	Mexican pattern
Food	Large	0.591 (rank 1)	0.533 (rank 1)	Primarily a supply-driven category in Shapiro (2024) .	Dominates both channels — the food-dominance pattern. Creates inflation swings only partially controllable through interest rates.
Energy	Medium	0.311 (rank 2)	0.267 (rank 2)	Primarily supply-driven in advanced economies.	Symmetric: Mexico produces oil for global markets and consumes it domestically, so energy amplifies both cyclical demand and supply pressures.
Services	Medium-large	0.257 (rank 3)	0.098 (rank 4)	Dominates demand-driven inflation in Shapiro (2024) .	Large average contribution (0.555 pp) but low correlation (0.463) — the services floor. Slow-moving; explains persistent disinflation resistance since 2023.
Manufacturing	Medium	0.209 (rank 4)	0.100 (rank 3)	Procyclical in most economies.	High demand-side correlation (0.691) but modest magnitude. Global value chain integration absorbs supply disruptions.
Housing	18.05%	0.054 (rank 5)	0.018 (rank 5)	Largest component of core CPI in the U.S.; strong monetary-policy response channel.	Housing non-response. Prices barely move; correlation with supply-driven inflation is even slightly negative (-0.082).

Source: Colunga-Ramos, Chen, and Perales (2026) , Table 1. Importance score = |correlation with aggregate inflation| x average contribution. Sample: November 2006 - July 2024.

Q1. Why is food so dominant in Mexican inflation compared to advanced economies?

Food dominates because it combines a large CPI weight with high sensitivity to both domestic demand cycles and global supply shocks — a pattern that developed-economy decomposition frameworks don’t capture.

The original decomposition framework, Shapiro (2024), developed for U.S. PCE inflation, finds services dominate demand-driven inflation while food and energy drive supply-driven swings . Colunga-Ramos, Chen, and Perales (2026) apply the same sign-restriction identification across 31 Mexican CPI sectors and find food ranks first for both demand (importance 0.591) and supply (importance 0.533) . This is the food-dominance pattern: the correlation of food with aggregate demand inflation reaches 0.756 and with supply inflation 0.771, and its average contribution dwarfs all other categories.

Three mechanisms drive this:

Mexico’s exposure to global commodity shocks — grain, meat, and shipping cost swings pass through to domestic food prices quickly.
Higher expenditure share on food in Mexican household budgets relative to advanced economies.
Food demand moves procyclically with the business cycle in a way U.S. services do, amplifying the demand-side contribution.

The policy implication is uncomfortable. Traditional monetary tightening works through demand channels, but when a category driven substantially by global supply disruptions also leads demand importance, interest rates alone are a blunt tool. Related work extends this logic to regional and manufacturing cuts of the Mexican economy — Chavarín, Gómez, and Salgado (2023) document sectoral demand dominance during the COVID-19 trough , and Colunga-Ramos and Torre Cepeda (2024) extend the analysis to regional manufacturing .

Q2. What explains Mexico’s slow disinflation since 2023 despite 725 basis points of tightening?

The services floor. Services contribute a large, low-volatility share of demand-driven inflation that adjusts slowly to monetary tightening, keeping headline inflation above target even after goods inflation normalizes.

Colunga-Ramos, Chen, and Perales (2026) show that Mexican services contribute an average 0.555 percentage points to demand-driven inflation but correlate only 0.463 with aggregate demand inflation — indicating high persistence but low cyclical amplitude . This combination is the services floor: services don’t spike, but they don’t retreat quickly either.

The 2023-2024 episode illustrates the dynamic. Goods inflation fell from 8.25% to 3.19% — a 5.06 percentage point decline driven by external supply normalization, where the supply component dropped from 3.52% to 1.20%. Services inflation barely moved, falling only from 5.01% to 4.71%, and the services demand component actually rose from 2.55% to 2.67% despite twelve months of policy rates at 11.25%.

The mechanism is textbook. Services are labor-intensive and prices are sticky (Nakamura and Steinsson, 2008 ). Mexican minimum wages rose 88% in real terms from 2019 to 2023, formal employment stayed strong, and unit labor costs grew roughly 1.5x productivity in services. Until labor markets slacken, the services floor persists regardless of policy rate levels.

The SVAR evidence supports the monetary transmission interpretation. A one-standard-deviation expansion in Mexico’s Divisia M2 raises demand-driven inflation by about 0.10 pp with a peak at month six and persistence through month fifteen, while supply-driven inflation remains statistically zero . The UV ratio declines for a year — the labor-market tightening channel that feeds back into services prices. This matches the standard monetary transmission literature (Christiano, Eichenbaum, and Evans, 1999 ).

Q3. Why does housing contribute so little to Mexican inflation despite being 18% of the CPI basket?

Housing prices in Mexico simply don’t move much. The correlation of housing with aggregate inflation is low (0.330 for demand, -0.082 for supply) and its average contribution is small, so the large basket weight does not translate into price dynamics.

Colunga-Ramos, Chen, and Perales (2026) find housing importance scores of 0.054 for demand-driven and 0.018 for supply-driven inflation — the lowest across the five categories, despite INEGI’s CPI methodology assigning housing 18.05% of the basket . This is the housing non-response.

Three structural features explain this:

A large share of Mexican dwellings are owner-occupied with implicit rent measured from construction-cost-indexed surveys that update slowly.
The rental market is thin and informal in many regions, dampening observed price adjustments.
Housing shows a slight negative correlation with supply-driven inflation (-0.082): supply shocks contract real incomes and reduce rental demand, softening housing prices when broader prices rise.

The policy implication is stark. The traditional monetary transmission channels through mortgage costs and housing wealth effects (Bernanke and Gertler, 1995 ) operate weakly in Mexico compared to the U.S., where shelter is the largest core CPI component and responds strongly to rates (Shapiro, 2024 ). The interest-rate-to-housing-to-consumption link that anchors much of Fed policy design has a much weaker counterpart at Banco de México.

Q4. How should an emerging-market central bank decompose inflation into supply and demand components?

Apply the sign-restriction logic of Shapiro (2024) at the sector level, then aggregate into economically meaningful groups afterward — don’t aggregate first and then decompose.

The core identification comes from microeconomics: a demand shift moves prices and quantities in the same direction along an upward-sloping supply curve, while a supply shift moves them in opposite directions along a downward-sloping demand curve. Colunga-Ramos, Chen, and Perales (2026) operationalize this with a rolling-window bivariate VAR (42 months, 12 lags) on log prices and log quantities for each of 31 CPI sectors . When sector-level residuals from both equations share a sign, the shock is demand-driven; when they differ in sign, it is supply-driven.

Practical recipe for replication in other EMs:

Disaggregate CPI to the finest sectoral level available and match each sector to a quantity proxy (industrial activity index, sector-level output, or services production indicator).
Estimate the rolling bivariate VAR on each sector; classify monthly shocks by residual-sign coincidence.
Aggregate sectoral contributions into five economically meaningful groups (food, energy, services, manufacturing, housing) using CPI weights. Avoid aggregating before decomposition — large sectors mechanically dominate and sign patterns lose identification power.
Construct an importance score = |correlation with aggregate inflation type| x average contribution, to rank what drives the swings.
Validate with a structural VAR: demand-driven measures should respond to domestic monetary variables, supply-driven measures to external supply proxies like the Global Supply Chain Pressure Index (Benigno, di Giovanni, Groen, and Noble, 2022 ).

The sectoral rankings are robust across alternative rolling windows (36, 42, 48, 60 months) and lag structures (6, 12, 18 lags), and also to Bayesian estimation with a Normal-Wishart prior. The framework also tracks inflation sources in near real time, a feature Banco de México researchers have extended to regional and manufacturing questions (Colunga-Ramos and Torre Cepeda, 2024 ; Chavarín, Gómez, and Salgado, 2023 ).

Q5. What SVAR ordering correctly identifies monetary policy shocks in an emerging market like Mexico?

Order external variables first (global supply, oil, U.S. CPI and industrial production, U.S. Divisia M2), then domestic inflation components, then domestic real activity, then domestic monetary aggregate, then exchange rate — with a block-recursive impact matrix that prevents domestic shocks from contemporaneously affecting external variables.

This ordering follows Kim and Roubini’s (2000) SVAR solution to exchange-rate and liquidity puzzles in small open economies , extending Cushman and Zha’s (1997) block-structure approach for Canada . Colunga-Ramos, Chen, and Perales (2026) use it to validate the decomposition: demand-driven inflation responds to Divisia M2 expansions, supply-driven inflation responds to GSCPI shocks, and the asymmetry holds across impulse response horizons .

Two features matter more than ordering choice:

Use Divisia monetary aggregates rather than a short-term interest rate. The choice of policy indicator matters more than most practitioners assume. Chen and Valcarcel (2021) show shadow federal funds rates produce persistent price puzzles in U.S. VARs , and Colunga-Ramos and Valcarcel (2024) produce the first Divisia M4 for Mexico and show it delivers sensible monetary responses without needing commodity-price controls . Chen and Valcarcel (2025) extend the rational-expectations framework that integrates Divisia with forward-looking inflation .
Control for COVID-19 dummies. April-June 2020 and April-May 2021 had IGAE growth exceeding three standard deviations; leaving them untreated distorts impulse responses.

Sign-restriction identification provides complementary validation. Uhlig (2005) pioneered sign restrictions on impulse responses , and Peersman (2005) applied the approach to supply, demand, monetary, and oil shocks . Colunga-Ramos, Chen, and Perales (2026) use this approach in their Appendix B to identify external U.S. supply and demand shocks, showing Mexican demand-driven inflation responds to U.S. demand shocks and Mexican supply-driven inflation to U.S. supply shocks — an external validation of the decomposition .

Q6. What historical episodes in Mexico validate the supply-demand inflation decomposition?

Three episodes — the 2008 Global Financial Crisis, the COVID-19 trough in 2020, and the 2024 disinflation surprise — show the decomposition offered policy-relevant guidance that aggregate inflation measures missed.

Colunga-Ramos, Chen, and Perales (2026) test three cases :

May 2020 — COVID trough. Headline inflation at 2.56% looked neutral, giving no clear policy signal. The decomposition showed supply-driven inflation at 2.39% and demand-driven inflation collapsed to 0.17% — a 93.4% supply share. This matched observable reality: global supply disruptions coexisted with Mexican GDP falling 8.5% in Q2 2020. Banco de México eased from 7.00% to 4.25% during 2020, correctly supporting collapsed demand while accepting that supply-driven inflation was beyond policy reach.

September 2008 - March 2010 — Global Financial Crisis. Headline inflation fell from 5.47% to around 3.8% over eighteen months. The decomposition attributes most of the decline to the demand component (3.12% to 1.84%) while supply-driven inflation fell less (2.35% to 1.92%). Food drove the demand-side collapse as households cut discretionary spending, consistent with the food-dominance pattern. Banco de México’s delayed easing — holding at 8.25% through late 2008 despite weakening demand — appears suboptimal in hindsight; the demand component had already begun falling by October 2008.

June-July 2024 — the disinflation head-fake. Headline inflation had fallen from 8.11% to 4.70% by June 2024, and markets priced in further cuts. The decomposition told a different story: demand-driven inflation stood at 2.53%, above its long-run average of 2.06%, while the supply component at 2.17% was doing most of the work. The next month, headline jumped to 5.22% as the demand component rose to 3.32% — exactly what the decomposition would have forecast. Banco de México held at 11.00% through the June 27 meeting and resumed cutting only in August.

The goods-services divergence over 2023-2024 completes the picture. Goods inflation fell 5.06 percentage points driven by supply normalization (shipping costs, peso appreciation), while services inflation barely moved and the services demand component actually rose . This is the services floor in operation: external supply shocks pass through goods quickly, domestic demand in labor-intensive services does not.

Q7. How do I replicate the rolling-window bivariate VAR sectoral decomposition step by step?

The decomposition has three stages: bivariate VAR estimation on each sector, residual-sign classification, and aggregation to economically meaningful groups — applied after decomposition, not before.

Colunga-Ramos, Chen, and Perales (2026) extend the Shapiro (2024) framework to 31 Mexican CPI sectors using a rolling 12-lag bivariate VAR on log prices and log quantities with a 42-month estimation window . The recipe:

Sector-level data assembly. Match each CPI sector to a monthly quantity proxy — for Mexico, INEGI’s IGAE components or sector-level industrial and services production indexes from the Banco de Información Económica.
Rolling bivariate VAR. For each sector and each end-of-window month t, estimate the 12-lag bivariate VAR using the 42 months ending in t. Shapiro (2024, JMCB) establishes this window; the paper documents robustness across 36, 42, 48, and 60 months.
Residual-sign classification. Save the contemporaneous residuals at t. Same sign = demand-driven (upward-sloping supply curve). Opposite sign = supply-driven (downward-sloping demand curve). Multiply each residual by its CPI weight to get the sector’s contribution.
Aggregation to five categories. Sum into food, energy, services, manufacturing, and housing using fixed CPI weights. Do not aggregate before decomposition — sectoral sign identification breaks if the data are collapsed first.
Importance score. Compute |correlation with aggregate inflation type| × average contribution for each category — the ranking metric the paper introduces.
External validation. Estimate a structural VAR with demand-driven and supply-driven series as separate variables; demand inflation should respond to domestic monetary expansions and supply inflation to the NY Fed Global Supply Chain Pressure Index .

Related questions: Where do I get sectoral CPI data for Mexico? · Can this method be applied to other emerging markets?

Q8. Where do I get sectoral CPI and quantity proxies for the Mexico decomposition?

All primary data sources are public: INEGI provides the CPI and quantity proxies, Banco de México provides monetary series, and external supply-chain data come from the NY Fed.

1. Sectoral CPI (INEGI, inegi.org.mx). The National Consumer Price Index (INPC) covers 299 generic items organized into 31 special-aggregate sectors, monthly since 1969 (current methodology from 2018). These 31 sectors map directly to the food, energy, services, manufacturing, and housing groups the paper uses.

2. Quantity proxies (INEGI’s IGAE and production indexes). Colunga-Ramos, Chen, and Perales (2026) match each CPI sector to a monthly quantity proxy from the Indicador Global de la Actividad Económica — Mexico’s monthly GDP equivalent — or from sector-level industrial and services production indexes in INEGI’s Banco de Información Económica. Sectors without a direct quantity proxy use the closest production indicator at the same frequency.

3. Monetary and financial data (Banco de México SIE, banxico.org.mx). The Sistema de Información Económica provides monthly Divisia M2, the policy interest rate, exchange rate, and inflation expectations from professional forecaster surveys. The Mexico Divisia M4 constructed in Colunga-Ramos and Valcarcel (2024) is the preferred monetary aggregate for the validation SVAR.

External data: The NY Fed’s Global Supply Chain Pressure Index is the primary external supply proxy; U.S. macro variables (CPI, industrial production) come from FRED; global oil prices use Brent and WTI from FRED. The sample runs November 2006 through July 2024.

COVID treatment: April–June 2020 and April–May 2021 had IGAE growth exceeding three standard deviations of the rolling distribution. Colunga-Ramos, Chen, and Perales (2026) treat these with dummy variables in the validation SVAR; leaving them untreated distorts impulse responses substantially.

Related questions: What is the Mexican Divisia M2? · What SVAR ordering identifies monetary policy shocks in Mexico?

Q9. Can this sectoral decomposition be applied to other emerging markets like Brazil, India, or Turkey?

The methodology is, in principle, portable to any economy with sectoral CPI and monthly quantity proxies spanning at least eight to ten years, though cross-country application is outside the scope of this paper.

Country readiness for the Colunga-Ramos, Chen, and Perales (2026) framework:

Brazil — IBGE publishes the IPCA with detailed sectoral breakdowns and sector-level industrial production (PIM-PF) at monthly frequency, making Brazil the most data-ready candidate. Given Brazil’s larger formal services sector, services may dominate demand-driven inflation more strongly than in Mexico, potentially reversing the food-dominance pattern.
India — MoSPI CPI with detailed components and Central Statistics Office IIP data support the same approach. India’s higher food expenditure share would likely amplify food’s dominance; the services floor’s magnitude will depend on the formal-informal employment composition, which varies across states.
Turkey — TÜİK CPI is available, but sectoral quantity proxies are sparser. The 2018–2024 currency-crisis episode would test whether the decomposition can separate demand inflation from supply-side passthrough under exchange-rate stress — a high-stakes test of the framework’s identification robustness.
South Africa, Indonesia, Chile, Colombia — all have the necessary statistical infrastructure; a cross-country panel would test whether the food-dominance pattern holds generally in emerging markets and whether the services floor’s magnitude correlates with formal-labor-market depth.

Related questions: What is the food-dominance pattern? · What historical episodes validate the decomposition in Mexico?

Q10. What does the food-services-housing decomposition imply for Banco de México’s monetary policy strategy?

Three concrete implications follow for any inflation-targeting central bank facing food-dominant and services-floor inflation: rate tightening is a blunt tool for supply-driven food inflation, the services floor demands patience, and the weak housing channel removes a standard stimulus option.

Colunga-Ramos, Chen, and Perales (2026) document three patterns that reshape policy design:

Real-time decomposition as a standing input. The sector-level supply/demand split can be computed in near-real-time once INEGI releases monthly CPI and IGAE data. A central bank running this in-house gains a systematic basis for the “is current inflation demand-driven?” question that otherwise depends on judgment calls in Monetary Policy Reports.
Forward guidance design for holds vs. cuts. When the demand-driven component is above its long-run average — as it was in June 2024 (2.53% vs. the 2.06% long-run mean) — the decomposition supports holding rates even as headline inflation declines. The July 2024 reacceleration documented in the paper would have been visible in real time: a hold signal, not a cut signal. This provides a public communication anchor: rates are elevated because demand inflation remains above trend, not because the bank is indifferent to food prices.
Supply-driven inflation and FX reserves. Food and energy supply shocks often pass through the exchange rate; since supply-driven inflation does not respond to domestic interest rates, reserve management and FX intervention decisions should be conditioned on the shock’s origin. The analogous argument for the U.S. — that money-growth rules become operational once the monetary signal is cleaned up — is developed in Belongia and Ireland (2022) , and the logic applies symmetrically to Banco de México.

Related questions: What is the services floor? · What historical episodes validate the decomposition?

Data and Code

Paper landing page and PDF: robinchen.org/publication/mexico-inflation-decomposition/ . For inquiries about replication data, contact zhengyang.chen@uni.edu .

Citation

Colunga-Ramos, Luis Fernando, Zhengyang Chen, and José Angel Perales. 2026. “Decomposing Supply and Demand Driven Inflation in Mexico: Evidence from Sectoral Analysis.” Economics Letters 264: 112980. https://doi.org/10.1016/j.econlet.2026.112980

@article{colungaramos2026decomposing,
 title={Decomposing Supply and Demand Driven Inflation in Mexico: Evidence from Sectoral Analysis},
 author={Colunga-Ramos, Luis Fernando and Chen, Zhengyang and Perales, Jos{\'e} Angel},
 journal={Economics Letters},
 volume={264},
 pages={112980},
 year={2026},
 publisher={Elsevier},
 doi={10.1016/j.econlet.2026.112980}
}

Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait-and-See for New Economic Data

Mon, 01 Dec 2025 00:00:00 +0000

Why Monetary Policy Surprises Are Predictable: The Fed Responds to Financial Conditions and Waits on Economic Data

TL;DR: High-frequency Fed policy surprises have been partially predictable from pre-FOMC data for three decades — a puzzle for the efficient market hypothesis. Chen (2026, Journal of Macroeconomics) resolves it: the Fed targets economic outcomes by responding primarily to financial conditions while adopting a wait-and-see stance on recent economic data. Markets take the dual mandate literally and miss this channel. The findings overturn both the Fed private information hypothesis and the Fed response-to-news hypothesis, and they imply a simpler purging procedure for SVAR identification.

Key Concepts

Wait-and-see channel: The Fed does not fully incorporate economic data released within ~2 weeks of an FOMC meeting; it waits for the data to show up in financial conditions first. Markets, expecting direct response, are systematically surprised. Chen (2026) .
Financial-conditions-sufficiency: Controlling for daily OFR Financial Stress Index and Treasury yield skewness exhausts the predictability of monetary policy surprises. Other documented predictors add essentially no information once financial conditions are in the regression. Chen (2026) .

Q1. Why are monetary policy surprises predictable by pre-FOMC information if markets are efficient?

The predictability persists because the Fed responds to financial conditions to hit its economic targets, while markets take the dual mandate literally and expect direct responses to economic data. This gap is structural, not a learning failure — which is why decades of observation have not closed it.

The puzzle itself is well-established: Bauer and Swanson document that a handful of pre-announcement variables predict a non-trivial share of high-frequency policy surprises , and Cieslak shows markets systematically underestimate the Fed’s response to economic fluctuations, especially in downturns . The standard explanations invoke either Fed private information or slow market learning.

Both explanations struggle with persistence. Chen (2026) resolves this by showing the Fed primarily reacts to financial conditions — which already embed market expectations and forward-looking information — while adopting a “wait-and-see” stance on recent economic data releases . Markets, taking Chair Powell’s “we don’t target financial conditions” literally, miss this channel entirely.

Three market blind spots generate the predictability:

Markets don’t account for how their own policy expectations feed into the Fed’s read of the economy
The time-varying relationship between financial conditions and economic outcomes is absorbed by the Fed but not by markets
Exogenous financial stress shocks trigger Fed responses markets don’t anticipate

Evidence snapshot: Controlling for a daily financial stress index and Treasury skewness alone reduces the predictive R² of the full Bauer-Swanson predictor set from ~12% to under 1% for scheduled FOMC meetings.

Three Explanations for Monetary Policy Surprise Predictability

Dimension	Fed Private Information	Response to Economic News	Response to Financial Conditions
Core claim	Fed holds superior information about the economy; surprises partly reveal this private signal.	Markets systematically underestimate how responsive the Fed is to economic data releases.	Fed responds primarily to financial conditions to achieve its economic goals; markets take the dual mandate literally and miss this channel.
Key references	Romer & Romer (2000) , Nakamura & Steinsson (2018) , Miranda-Agrippino & Ricco (2021)	Cieslak (2018) , Bauer & Swanson (2023b) , Schmeling et al. (2022)	Caldara & Herbst (2019) , Brunnermeier et al. (2021) , Caballero et al. (2024) , Chen (2026)
Testable prediction	Predictors of surprises contain information not already in market prices.	Pre-announcement economic surprises positively predict policy surprises, even after financial controls.	Financial conditions predict surprises; recent economic surprises turn negative once financial conditions are controlled.
Empirical verdict	Rejected. Greenbook forecasts lose predictive power after controlling for public info ; Bauer-Swanson predictors already explain 57% of pre-FOMC FSI variation .	Not supported once financial conditions enter. Real-activity surprises within 14 days flip to a negative coefficient , opposite to the news-response sign.	Supported. FSI + Treasury skewness alone drive R² from ~12% to <1% relative to the full Bauer-Swanson set ; sign on FSI is consistently dovish-to-stress.
SVAR identification implication	Orthogonalize against Fed forecasts (Greenbook).	Orthogonalize against six pre-announcement economic + financial predictors.	Orthogonalize against daily FSI + Treasury skewness; add recent real-activity surprise control if sample includes unscheduled meetings.
Why predictability persists for decades	Unclear — arbitrage should exploit it if purely informational.	Unclear — markets should eventually learn the true reaction parameter.	Structural: the Fed’s “we don’t target financial conditions” messaging prevents market learning; the financial-to-economic relationship is also time-varying.
Named concept	Fed information effect	Fed response-to-news effect	Wait-and-see channel · Financial-conditions-sufficiency (Chen 2026 )

Q2. Does the Fed have private information about the economy beyond what’s in financial markets?

No — the pre-announcement variables that predict policy surprises are already priced into daily financial conditions, so they cannot be the Fed’s private information.

The “Fed information effect” originates with Romer and Romer, who found Fed forecasts outperform commercial forecasts for inflation , and was sharpened by Nakamura and Steinsson, who interpret the positive co-movement of surprises and private GDP forecasts as evidence the Fed reveals information . Miranda-Agrippino and Ricco build on this by orthogonalizing surprises against Greenbook forecasts .

The evidence has eroded this view. Bauer and Swanson show Greenbook forecasts lose predictive power after controlling for public information , and Lunsford finds the information effect holds in the early 2000s but not afterward . Cieslak and Schrimpf decompose surprises and find information shocks play a minor role at FOMC announcements .

Chen (2026) provides direct evidence against private information: the six strong predictors in Bauer and Swanson explain 57% of variation in the OFR Financial Stress Index the day before FOMC meetings, meaning their information content is already embedded in market prices . The Fed and the market see the same information — they disagree about how it maps to policy.

A related reinterpretation: Jarociński and Karadi’s “information shock” component (JK_Info), which comoves with stocks , is itself strongly predicted by pre-announcement financial stress in Chen’s data — suggesting it reflects the Fed’s response to financial conditions rather than exclusive Fed knowledge.

Q3. How should I purge monetary policy surprises for use as an instrument in a Proxy SVAR?

Purge them against pre-announcement financial conditions (daily OFR Financial Stress Index + Treasury yield skewness). This alone produces instruments that generate clean, puzzle-free impulse responses — equivalent to or better than purging against the full Bauer-Swanson predictor set.

The identification problem is well-known. Gertler and Karadi use high-frequency surprises as external instruments in a Proxy SVAR , but Caldara and Herbst show that failing to account for the Fed’s systematic response to credit spreads attenuates estimated monetary policy effects . Bauer and Swanson’s solution is to orthogonalize MPS against six pre-announcement predictors (yield curve slope, S&P 500, commodity prices, employment growth, nonfarm payroll surprise, Treasury skewness) .

Chen (2026) shows that orthogonalizing Nakamura-Steinsson surprises against just two daily financial variables yields impulse responses free of price and output puzzles — and in fact more conventional at short horizons than the Bauer-Swanson-orthogonalized version . This is what the paper terms financial-conditions-sufficiency: once financial information is purged, additional economic predictors add little.

Practical recipe:

Start with a raw high-frequency surprise (NS , MPS , or GSS target/path factor )
Regress it on the OFR FSI level and 30-day Treasury skewness average the day before each FOMC announcement
Use the residuals as your external instrument
If your sample includes unscheduled meetings, add a control for the Scotti real-activity surprise index on the day before the meeting — the wait-and-see channel is stronger there

Q4. Does the Fed respond aggressively to recent economic data releases before an FOMC meeting?

No — the Fed adopts a “wait-and-see” approach for data released within roughly two weeks of the meeting, fully incorporating only data released three or more weeks prior. Markets misread this as aggressive responsiveness.

The dominant view, formalized by Cieslak and Bauer and Swanson , is that markets systematically underestimate the Fed’s response to economic news, producing positive co-movement between pre-announcement economic surprises and policy surprises. Schmeling, Schrimpf and Steffensen similarly document expectation errors consistent with underreaction .

Chen (2026) finds the opposite sign once financial conditions are controlled: a positive real activity surprise in the two weeks before an FOMC meeting predicts a dovish policy surprise, not hawkish . This reverses the sign predicted by the “response to news” hypothesis and identifies what the paper calls the wait-and-see channel.

Timing evidence (Chen 2026):

Real surprises 1–14 days pre-meeting → significantly negative coefficient (Fed waits, market expects hike, Fed disappoints)
Real surprises 21–28 days pre-meeting → insignificant or positive (Fed has incorporated, market correctly anticipates)
Pattern is sharper for the MPS measure (which includes unscheduled meetings) than for NS (scheduled only)

Implication for identification: If you’re running event studies around unscheduled meetings, control for recent real activity surprises alongside financial conditions. The wait-and-see effect is concentrated there.

Q5. Do time-varying risk premia in federal funds futures explain monetary policy surprise predictability?

No — the empirical pattern runs the wrong way. Risk premia respond to monetary policy surprises after the announcement, rather than generating them.

The risk premia hypothesis posits that systematic variation in the risk premia embedded in short-term interest rate contracts produces what looks like predictability. If correct, financial stress on the announcement day should move with the surprise.

It doesn’t. Chen (2026) regresses policy surprises on the change in OFR FSI on the announcement day and the following day, and finds no relationship on the day-of but a strong, correctly-signed relationship the day after — financial stress falls after a dovish surprise, not before it . The FSI barely moves on FOMC days themselves.

This aligns with prior skepticism. Bauer and Swanson argue the required risk premia variation is implausibly large , and Piazzesi and Swanson show fed funds futures risk premia are small . It also fits the broader literature documenting policy-to-risk-premia transmission: Bernanke and Kuttner on equity reactions , Hanson and Stein on long rates , and Drechsler, Savov and Schnabl on the risk-taking channel .

Bottom line: Risk premia are a consequence of policy surprises, not their source.

Q6. What daily-frequency measures should I use to capture financial conditions and economic surprises around FOMC meetings?

Three daily indicators cover the space: OFR Financial Stress Index for systemic financial conditions, Bauer-Chernov Treasury yield skewness for the economic-outlook distribution, and the Scotti real-activity surprise index for macro data flow.

High-frequency FOMC event studies have long suffered a trade-off. Miranda-Agrippino and Ricco address information insufficiency with dynamic factor models on monthly macro data , but monthly data can’t be causally linked to irregular meeting dates. Chen (2026) argues a daily, information-rich combination resolves this .

The three measures:

OFR Financial Stress Index (Monin 2019) — daily, global coverage across credit, equity, funding, safe assets, and volatility. Decomposable into five sub-indexes. Available from January 2000. Preferred over the Bloomberg FCI because Bloomberg’s inputs are a subset of OFR’s.
Treasury yield skewness (Bauer and Chernov 2024) — option-implied skewness of 10-year Treasury yields. Captures higher-moment information about economic-outlook risks (upside vs downside) that the FSI’s first-moment measure misses.
Scotti real-activity surprise index — daily, aggregates surprises in GDP, industrial production, employment, retail sales, and PMIs using time-varying weights. Available from June 2003. Includes an intuitive time-decay in the impact of each data release.

Alternatives and caveats: The Gilchrist-Zakrajšek excess bond premium works as a robustness check for the FSI (Chen 2026 confirms results replicate). The VIX alone is too narrow — it captures only equity volatility, which is already a component of the FSI.

Q7. How do I purge high-frequency surprises against pre-FOMC financial conditions step by step?

Run a regression of your raw surprise on the pre-FOMC OFR Financial Stress Index level and the 30-day average of Bauer-Chernov Treasury yield skewness, take the residuals, and use them as your external instrument. Chen (2026) shows this two-variable purge produces puzzle-free impulse responses equivalent to or better than the six-variable Bauer-Swanson purge .

Concrete recipe:

Pull your raw high-frequency surprise series — Kuttner (2001) , Nakamura-Steinsson (2018) , Bauer-Swanson MPS (2023) , or Jarociński-Karadi (2020) .
Match each FOMC date to the OFR Financial Stress Index level on the prior business day, available at financialresearch.gov/financial-stress-index/ .
Match each FOMC date to the Bauer-Chernov (2024) Treasury yield skewness , averaged over the 30 days before the meeting, available at the FRB San Francisco Treasury Yield Skewness page.
Regress surprise ~ FSI_t-1 + TreasurySkew_t-30:t-1 via OLS; save residuals.
If your sample includes unscheduled meetings, add a control for the Scotti (2016) real-activity surprise index on the prior business day — the wait-and-see channel is concentrated in unscheduled-meeting windows.

Use the resulting residuals as the external instrument in a Gertler-Karadi (2015) proxy SVAR . Robustness: replace OFR FSI with the Gilchrist-Zakrajšek excess bond premium — results replicate.

Related questions: Where do I download FSI and Treasury skewness data? · Does this purge work for ECB and BoE surprises?

Q8. Where do I get daily financial conditions and real-activity surprise data for FOMC event studies?

Three sources cover the full toolkit needed to replicate or extend Chen (2026) , all publicly available and freely downloadable.

OFR Financial Stress Index — daily from January 2000, decomposable into credit, equity, funding, safe-assets, and volatility sub-indexes. Monin (2019) documents the construction . Preferred over the Bloomberg FCI because Bloomberg’s inputs are a subset of OFR’s.
Bauer-Chernov Treasury yield skewness — daily option-implied skewness of 10-year Treasury yields, published by FRB San Francisco. Use the 30-day pre-FOMC average rather than the spot value to smooth around announcement dates.
Scotti real-activity surprise index — daily, aggregates GDP, IP, employment, retail sales, and PMI surprises with time-varying weights, available from FRB San Francisco.

For the raw surprises themselves: Bauer-Swanson MPS and Nakamura-Steinsson are available at the authors’ websites; Jarociński-Karadi from the AEJ:Macro data archive. For ECB equivalents, use the Altavilla et al. Euro Area Monetary Policy Event-Study Database (EA-MPD).

Related questions: How does the purge differ for unscheduled vs. scheduled meetings? · Does the OFR FSI work as a robustness check against EBP?

Q9. Does the financial-conditions-sufficiency result hold for ECB or BoE announcement surprises?

Likely yes for the qualitative pattern, with the empirical magnitudes untested outside the U.S. Chen (2026) is U.S.-only , but the structural argument — central banks respond to financial conditions to hit their economic targets, markets miss this channel — is not U.S.-specific. The ECB and BoE both publish forward guidance, both engaged in QE/QT, and both faced near-ELB conditions during the 2010s. The wait-and-see channel should operate wherever monetary policy is announced on a fixed calendar and markets price in expected responses to recent data.

The natural empirical extension uses the Altavilla et al. Euro Area Monetary Policy Event-Study Database for ECB surprises and the Cesa-Bianchi, Thwaites, Vicondoa (2020) UK monetary surprises for the BoE. Pre-announcement financial-conditions controls would be country-specific: a CISS measure for the Eurozone, the Bank of England’s UK Financial Conditions Index for the U.K.

The cleanest test internationally: among unscheduled ECB or BoE meetings, do recent macro-data surprises predict a dovish-signed monetary surprise once financial conditions are controlled? If yes, the U.S. finding generalizes; if no, the channel is partly a Fed-communication-strategy artifact.

Related questions: How do I download EA-MPD data? · Does the wait-and-see channel survive in unscheduled-meeting samples?

Q10. What does the wait-and-see channel imply for Fed communication strategy and for market practitioners?

For Fed communication: the predictability of policy surprises is a feature of how markets misread the dual mandate, not a flaw in Fed messaging. Chen (2026) argues that markets take the “we don’t target financial conditions” statement literally and miss the channel — the gap is structural and not closed by market learning .

For market practitioners, three actionable implications:

Pre-FOMC positioning. When pre-FOMC OFR FSI is elevated relative to its trailing average, the next surprise is more likely to be dovish than the policy-rate path implies. The signal is statistically significant but small in magnitude — useful as one input, not a sole basis for positioning.
Recent data surprises before unscheduled meetings. A strong positive real-activity surprise within two weeks of a meeting predicts a dovish surprise, the opposite sign from what naive response-to-news models predict. This is the wait-and-see channel, sharpest for unscheduled meetings.
Risk-premium narrative caution. Financial-stress and policy-surprise comovement is post-announcement, not pre-announcement — supporting Bauer-Swanson’s prior skepticism and Piazzesi-Swanson’s small-magnitude finding . Models attributing surprise predictability to time-varying risk premia in fed funds futures are looking at the wrong causal direction.

Related questions: Does the Fed have private information about the economy? · What does the response-to-news hypothesis miss?

Data and Replication

All data and code for Chen (2026) are available at robinchen.org . The paper uses:

OFR Financial Stress Index (daily, 2000–present)
Bauer-Chernov Treasury Yield Skewness (daily)
Scotti real-activity surprise index (daily, 2003–present)
Standard high-frequency monetary policy surprise series: Kuttner, Nakamura-Steinsson, Bauer-Swanson MPS, Jarociński-Karadi, and GSS target/path factors

Citation

Chen, Zhengyang. 2026. “Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait and See for New Economic Data.” Journal of Macroeconomics 87: 103736. https://doi.org/10.1016/j.jmacro.2025.103736

@article{chen2026demystifying,
 title={Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait and See for New Economic Data},
 author={Chen, Zhengyang},
 journal={Journal of Macroeconomics},
 volume={87},
 pages={103736},
 year={2026},
 publisher={Elsevier},
 doi={10.1016/j.jmacro.2025.103736}
}

From Disruption to Integration: Cryptocurrency Prices, Financial Fluctuations, and Macroeconomy

Tue, 01 Jul 2025 00:00:00 +0000

How Cryptocurrency Markets Now Drive Macroeconomic Outcomes

TL;DR: Cryptocurrency has crossed the threshold from isolated digital experiment to systemically important financial asset. Chen (2025, Journal of Risk and Financial Management) uses a Bayesian SVAR with Pandemic Priors to show that cryptocurrency price shocks explain 18% of equity, 27% of commodity, and 18% of long-horizon inflation variance over 2015–2024, with sentiment-driven shocks dominant and regulatory effects negligible. Real economic effects on industrial production and unemployment exist but are modest.

Key Concepts

Cryptocurrency-as-systematic-risk-factor: The empirical result that cryptocurrency now functions as a systematic source of variance in equity (17.7%) and commodity (27.2%) markets over 2015–2024, rather than a portfolio diversifier — evidence that cryptocurrency has crossed from isolated asset to systemically integrated risk factor. Chen (2025) .
Sentiment-dominant transmission: The finding that, across narrative-identified shock categories from 67 major crypto-market events (2014–2023), sentiment shocks (coefficient 1.36, t = 3.15) are the strongest driver of cryptocurrency price movements, with technology shocks second and regulatory shocks statistically insignificant. Chen (2025) .
Pandemic-prior cryptocurrency identification: The methodological adaptation of Cascaldi-Garcia (2022) Pandemic Priors — which extend the Minnesota prior with time dummies controlled by hyperparameter φ — to handle COVID-era extreme observations in a Bayesian SVAR identifying cryptocurrency macroeconomic transmission. Chen (2025) .

Three Views of Cryptocurrency’s Macroeconomic Role

Dimension	Diversifier view	Speculative-only view	Systematic-risk-factor view (Chen 2025)
Core claim	Crypto provides portfolio diversification due to low correlation with traditional assets.	Crypto is a speculative asset with no fundamental macroeconomic role.	Crypto has crossed into systemic importance with measurable spillovers to equity, commodity, and inflation variance.
Key references	Bouri et al. (2017) ; Charfeddine et al. (2020)	Early speculative-bubble literature	Chen (2025) , grounded in Baker & Wurgler (2007)
Empirical verdict	Diversification benefits weaken sharply during stress; crypto-equity comovements are positive, not negative, in 2015–2024.	Cannot explain the 17.7–27.2% equity/commodity variance contributions in modern data.	Supported. Variance decompositions show systemic transmission; narrative validation confirms sentiment and technology as drivers.
Policy implication	No special monetary or regulatory framework needed.	Monitor for fraud only; macroeconomic role irrelevant.	Central banks should monitor crypto for inflation pressure; financial regulators should treat it as a source of systemic risk.

Note: Diversifier view reflects pre-2018 literature. Chen (2025) covers January 2015 – November 2024.

Q1. Has cryptocurrency become a systematically important financial asset?

Yes. Chen (2025) finds that cryptocurrency price shocks explain 17.7% of S&P 500 forecast-error variance at 6 months and 27.2% of CRB commodity variance at 30 months — placing crypto alongside traditional macro shocks as a first-order driver of financial-market fluctuations.

This finding overturns the early-literature claim that cryptocurrency offers diversification benefits. Bouri et al. (2017) originally characterized Bitcoin as a hedge against global uncertainty, and Charfeddine, Benlagha, and Maouchi (2020) found weak, time-varying cross-correlations with conventional assets consistent with diversification. The 2015–2024 sample in Chen (2025) spans the institutional-adoption era — spot Bitcoin ETFs, corporate treasury holdings, derivatives integration — and yields the opposite conclusion: cryptocurrencies have become systematic risk amplifiers, aligned with the contagion-vs-interdependence distinction formalized by Forbes and Rigobon (2002) .

The empirical fingerprint is a drop in the Financial Stress Index on impact followed by recovery. This pattern — stress alleviates, not intensifies, with a positive crypto shock — is consistent with a risk-on channel operating through intermediary balance sheets, as described in Adrian and Shin (2010) and Brunnermeier and Pedersen (2009) . Crypto shocks also explain 5.7% rising to 8.2% of the Financial Stress Index variance, a modest but statistically meaningful share.

The scale of equity and commodity variance contributions (17.7% and 27.2%) is quantitatively comparable to the contributions of monetary policy and aggregate demand shocks in standard macro VARs, marking a structural break from the pre-2015 period when cryptocurrency had negligible macro spillovers.

Q2. What drives cryptocurrency price shocks — sentiment, technology, or regulation?

Sentiment dominates. Chen (2025) classifies 67 major crypto-market events from 2014–2023 into six categories and finds only sentiment shocks (coefficient 1.36, t = 3.15) and technology shocks (coefficient 1.02, t = 2.06) significantly explain the identified structural crypto-shock series. Regulatory, monetary, infrastructure, and network-effect shocks are all statistically insignificant.

The narrative identification follows Romer and Romer’s (2004) approach to monetary policy shocks, coding each event as +1 (favorable), −1 (unfavorable), or 0 (absent) in a given month. The six categories are: technology (protocol upgrades, hard forks, outages), sentiment (institutional adoption announcements, mainstream coverage, exchange collapses), regulatory (legal recognition, bans, enforcement), monetary (central bank moves affecting alternative-asset demand), infrastructure (exchange launches, custody solutions), and network effects (adoption milestones, integrations).

Sentiment dominance validates Baker and Wurgler’s (2007) investor-sentiment framework — retail-dominated asset markets exhibit amplified price movements beyond fundamentals. It partially contradicts papers like Borri and Shakhnov (2020) and Chokor and Alfieri (2021) that emphasize regulation as a primary driver: Chen (2025) finds regulatory event dummies are statistically insignificant after controlling for the full SVAR system.

The significant technology coefficient establishes that cryptocurrency is not a pure speculative bubble — protocol upgrades and technical improvements generate measurable economic value, consistent with Caporale, Gil-Alana, and Plastun (2018) , who documented persistence in the cryptocurrency market consistent with technology-based fundamentals.

Q3. How does cryptocurrency transmit to the real economy?

Through a dual channel: sentiment drives financial-market integration and technology drives real-economy effects. Chen (2025) documents that a one-standard-deviation positive Bitcoin price shock produces a sustained 1.2% rise in the S&P 500, a 2% rise in the CRB commodity index, a delayed 0.15% rise in industrial production, a persistent 0.02% decline in unemployment, and a 0.15% rise in the PCE price index over a 30-month horizon.

Two theoretical frames ground the financial-market response. Markowitz’s portfolio theory and Sharpe’s CAPM predict that assets with similar systematic risk exposures comove, which reframes cryptocurrency as an integrated risk asset rather than an isolated instrument. Behavioral extensions come from Baker and Wurgler’s (2007) investor-sentiment framework, where mood-driven trading creates systematic factors affecting all risky assets.

The real-economy transmission is quantitatively modest but theoretically well-grounded in investment-channel mechanics from Jermann and Quadrini (2012) and uncertainty-channel mechanics from Bloom (2009) , where asset-price volatility creates real-options effects on investment timing. The asymmetry between the large financial-market response (17.7–27.2% variance shares) and the modest real-economy response (6.2% industrial production, 3.8% unemployment at 30 months) reflects how each channel works: the financial-market response operates within days through correlated asset repricing and intermediary balance-sheet adjustments, while the real-economy response requires investment and hiring decisions with inherent multi-month lags.

Three empirical signatures distinguish the transmission mechanism:

Immediate: equity (+1.2%), commodities (+2%), financial stress drops on impact, then recovers.
Delayed but persistent: industrial production rises ~0.15% with a multi-month lag; unemployment falls ~0.02% persistently.
Cumulative inflation: the contribution of crypto shocks to price-level forecast-error variance grows from 3.6% at 6 months to 17.6% at 30 months — a signature of demand-side transmission, not transitory financial noise.

Q4. Why use Bayesian SVAR with Pandemic Priors for this question?

Standard VARs fail when the sample includes COVID-era extreme observations. Cascaldi-Garcia (2022) proposes extending the Minnesota prior with time dummies for the pandemic period, controlled by a hyperparameter φ that governs how much signal the model extracts from pandemic observations — as φ → 0 the pandemic period is treated as exceptional and its variance is absorbed by the dummies; as φ → ∞ the setup reverts to a conventional Minnesota prior.

Chen (2025) selects φ = 0.1 via marginal-likelihood maximization over a grid from 0.001 to 500, and shows that setting φ = 500 (the Minnesota-prior limit) produces materially different real-economy impulse responses — less persistent declines in unemployment and industrial production, more contractionary Divisia M4 movement. The data strongly favor the Pandemic Priors specification, confirming that how one handles COVID-19 observations affects the estimated transmission of cryptocurrency shocks to macroeconomic variables.

The monthly SVAR includes eight variables ordered recursively: PCE price index, unemployment rate, industrial production, Divisia M4, cryptocurrency price, S&P 500, CRB commodity index, and the St. Louis Fed Financial Stress Index. The prior follows the dummy-observation implementation from Bańbura, Giannone, and Reichlin (2010) , extended with Cascaldi-Garcia’s time-dummy block for the pandemic period. Overall tightness λ = 0.2; optimal φ selected by maximum marginal likelihood; impulse responses at 30-month horizon with 68% posterior probability bands.

Robustness checks confirm the main findings are stable under: alternative variable orderings (crypto ordered last produces virtually indistinguishable impulse responses); CPI instead of PCE for the price level; excess bond premium (Gilchrist and Zakrajšek 2012 ) or Cleveland Fed FSI instead of St. Louis FSI; and narrative validation via Romer-Romer-style event regression on six categories of crypto-market events.

Q5. What does cryptocurrency’s macro role mean for monetary policy?

Central banks should monitor cryptocurrency markets for demand-driven inflation pressure. With cryptocurrency shocks explaining 18% of long-horizon inflation variance, the asset class has crossed the threshold of monetary-policy relevance. Chen (2025) finds the contribution to PCE price-level forecast-error variance rises from 3.6% at 6 months to 17.6% at 30 months, while S&P 500, CRB, and Financial Stress Index shocks combined contribute 10.1% at 30 months — making cryptocurrency the largest single non-own driver of price-level variance in this sample.

The inflation mechanism matches New Keynesian demand-side transmission: positive crypto shocks raise equity and commodity prices, ease financial stress, stimulate investment and consumption, and pass through to aggregate-demand-driven inflation via the wealth channel (Case, Quigley, and Shiller 2005 ) and the financial-accelerator channel (Bernanke, Gertler, and Gilchrist 1999 ).

Divisia M4 shows initial expansion followed by contraction after a positive crypto shock — evidence of endogenous monetary tightening, but not aggressive enough to offset the price effect. Chen and Valcarcel (2021) argue Divisia aggregates are the correct monetary indicator when short rates are uninformative, and Chen and Valcarcel (2025) document their superior information content relative to simple-sum measures. The implication is that the Fed’s accommodative response leaves meaningful crypto-driven inflation in the system.

The demand-driven nature of the inflationary impulse distinguishes it from a transitory financial-market disturbance and makes it policy-actionable. Monetary authorities should incorporate cryptocurrency developments into their inflation forecasting models, and financial regulators should monitor the cryptocurrency market as a source of systematic risk given its substantial contribution to financial-market volatility.

Q6. Does the integration result hold beyond Bitcoin?

The current paper covers Bitcoin only. Bitcoin’s dominant market capitalization during 2015–2024 — averaging 40–65% of total cryptocurrency market cap over the sample — and the need for a sufficiently long monthly time series for structural VAR identification motivated this scope. Bitcoin’s market dominance makes the results broadly descriptive of the overall cryptocurrency market for this period.

Whether the results generalize to the full cryptocurrency ecosystem involves two open questions. First, altcoins, stablecoins, and DeFi tokens have different fundamental characteristics and may transmit to macro variables through different channels or with different magnitudes. Second, the cryptocurrency market structure changed substantially over the 2015–2024 window — from Bitcoin-dominated speculation to institutionally integrated infrastructure — and continuation samples will likely show evolving transmission dynamics as institutional adoption deepens further.

The paper’s main findings are robust to alternative variable orderings, price-level measures (CPI vs. PCE), and financial-stress indicators (excess bond premium vs. St. Louis FSI vs. Cleveland FSI), suggesting the Bitcoin-specific result is not an artifact of particular specification choices. Extending the framework to other cryptocurrency assets as data availability improves remains an important direction for future empirical work.

Q7. How do I estimate a Bayesian SVAR with Pandemic Priors for cryptocurrency shock analysis?

The setup combines a standard BVAR with Cascaldi-Garcia (2022) Pandemic Priors , which down-weight COVID-period observations to prevent them from contaminating impulse-response estimates while preserving the information they carry about volatility.

Chen (2025) implements this in five steps:

Construct a monthly panel of cryptocurrency price (Bitcoin), traditional financial market variables (equity, commodity prices, financial stress index), and macro variables (industrial production, unemployment, PCE).
Specify the BVAR with Minnesota-style shrinkage on the coefficients, plus Pandemic Priors that introduce additional shrinkage on COVID-period error variances (March 2020 through approximately mid-2021), using the dummy-observation implementation of Bańbura, Giannone, and Reichlin (2010) .
Identify cryptocurrency shocks via recursive ordering — crypto last among financial market variables but before macro real activity — and validate with Romer and Romer (2004) narrative identification matched against 67 documented crypto-market events.
Estimate via Gibbs sampling with overall tightness λ = 0.2; select the Pandemic Prior hyperparameter φ = 0.1 by marginal-likelihood maximization.
Report impulse responses with 16/84 credible bands and forecast error variance decompositions at 12-, 24-, 36-month horizons.

Why Pandemic Priors matter here: cryptocurrency markets experienced extreme volatility in March 2020 that would dominate a standard BVAR’s estimated dynamics. Setting φ = 500 (conventional Minnesota limit) materially changes real-economy impulse responses — less persistent unemployment declines, more contractionary Divisia M4 — confirming the priors are necessary for this sample.

Related questions: What cryptocurrency price series is appropriate for an SVAR? · How does narrative identification validate the recursive ordering?

Q8. Which cryptocurrency price and macro variables are appropriate for systemic-risk SVAR analysis?

For the cryptocurrency variable, Bitcoin’s log price is the standard choice given its dominant 40–65% market capitalization share during 2015–2024. Chen (2025) uses an eight-variable monthly SVAR ordered recursively: PCE price index, unemployment rate, industrial production, Divisia M4, Bitcoin price, S&P 500, CRB commodity index, and the St. Louis Fed Financial Stress Index.

Data sources:

Cryptocurrency prices: CoinMarketCap (daily, aggregated to monthly).
Traditional financial markets: S&P 500 and CRB commodity index from FRED ; OFR Financial Stress Index .
Macro variables: industrial production (INDPRO), unemployment (UNRATE), PCE (PCEPI) from FRED.
Monetary aggregate: Divisia M4 from CFS AMFM .
Sample period: January 2015 onward — earlier data has too little institutional adoption to identify the integrated regime.

Variable selection cautions: do not include trading volume in the SVAR (it breaks identification); do include a financial stress measure in addition to equity prices, as they capture distinct channels; for research on monetary-policy effects on crypto, add a policy indicator using Divisia M4 following Chen and Valcarcel (2021) .

Related questions: How are Pandemic Priors implemented? · Does the result hold if Bitcoin is replaced by Ethereum?

Q9. Does the cryptocurrency-macro spillover result extend to altcoins, DeFi protocols, or stablecoins?

Likely yes for altcoins, more nuanced for DeFi, and structurally different for stablecoins — but the empirical evidence is sparse and a natural extension of Chen (2025) .

Altcoins typically comove strongly with Bitcoin in return space, so the spillover pattern should replicate at smaller magnitudes. A natural extension applies the BSVAR with Bitcoin replaced by Ethereum or a market-cap-weighted top-10 index. Ethereum, with its DeFi infrastructure role, may show distinct dynamics that warrant separate identification.

DeFi protocols introduce additional channels — total value locked, governance token dynamics, liquidation cascades during stress — that a price-only SVAR misses. The right extension would add aggregate DeFi TVL and a measure of leverage in lending protocols.

Stablecoins are structurally different: their price shocks are small (depegging events are large but rare), and the relevant shock is the supply of stablecoins. A large stablecoin issuance amounts to mechanical T-bill demand — making the right framework closer to a money-supply shock in traditional monetary economics than a risk-asset price shock.

Cross-country considerations: cryptocurrency adoption rates vary enormously. The U.S. results in Chen (2025) likely overstate the macro effect in low-adoption economies and understate it in high-adoption ones.

Related questions: How does DeFi affect monetary transmission? · What are stablecoins’ systemic risk implications?

Q10. What does cryptocurrency’s 18% inflation variance contribution imply for monetary policy and financial regulators?

For monetary policy: the result implies that cryptocurrency markets have moved to a quantitatively significant input into the inflation process, and central banks should monitor crypto-driven financial conditions alongside traditional credit and equity measures. Chen (2025) documents that positive Bitcoin price shocks generate persistent inflationary pressure — a 0.15% rise in the PCE price level over a 30-month horizon — operating through wealth and investment channels. The 18% long-horizon inflation variance contribution grows from 3.6% at 6 months to 17.6% at 30 months, making cryptocurrency the largest single non-own driver of price-level variance in this sample.

Concrete implications for central-bank monitoring:

Include crypto-driven financial conditions in the dashboard. The OFR Financial Stress Index does not currently include crypto-specific volatility; an extension would improve real-time signal.
Recognize crypto wealth effects in consumption forecasting. With large retail crypto holdings, even modest wealth elasticities translate to first-order consumption effects.
Distinguish sentiment-driven from technology-driven crypto shocks. Chen (2025) finds sentiment shocks dominate and produce the inflation spillover; technology shocks are smaller in magnitude.

For financial regulators: prudential rules for bank crypto exposure, stablecoin reserve requirements, and stress-test scenarios all need to account for the documented spillover magnitudes. The demand-driven nature of the inflationary impulse — as opposed to a transitory financial-market disturbance — makes it policy-actionable rather than a noise term.

Related questions: What is the wealth-effect channel for cryptocurrency? · How does Divisia M4 respond to crypto shocks?

This paper situates cryptocurrency within Chen’s broader research program on monetary transmission and financial market integration. Chen (2026, Journal of Macroeconomics) examines how the Federal Reserve responds to financial conditions in setting policy — a transmission channel through which cryptocurrency-driven volatility could affect monetary decisions. Chen and Valcarcel (2021, JEDC) and Chen and Valcarcel (2025, JEDC) develop the structural VAR identification methods that this paper extends to cryptocurrency markets.

Data and Replication

Bitcoin price data: CoinMarketCap (daily, aggregated to monthly)
Macroeconomic series: FRED — PCE price index, CPI, unemployment, industrial production, S&P 500, CRB commodity index, St. Louis Fed FSI, Cleveland Fed FSI, excess bond premium
Divisia monetary aggregates: Center for Financial Stability — AMFM dataset , Divisia M4
Pandemic Priors implementation: Cascaldi-Garcia (2022) , φ = 0.1, λ = 0.2, 30-month impulse horizons, 68% posterior bands
Sample: Monthly, January 2015 – November 2024
Open access: UNI ScholarWorks · Journal of Risk and Financial Management

Citation

Chen, Zhengyang. 2025. “From Disruption to Integration: Cryptocurrency Prices, Financial Fluctuations, and Macroeconomy.” Journal of Risk and Financial Management 18(7): 360. https://doi.org/10.3390/jrfm18070360

@article{chen2025crypto,
 author = {Chen, Zhengyang},
 title = {From Disruption to Integration: Cryptocurrency Prices, Financial Fluctuations, and Macroeconomy},
 journal = {Journal of Risk and Financial Management},
 volume = {18},
 number = {7},
 pages = {360},
 year = {2025},
 publisher = {MDPI},
 doi = {10.3390/jrfm18070360},
 url = {https://doi.org/10.3390/jrfm18070360},
 license = {CC BY 4.0}
}

Modeling Inflation Expectations in Forward-Looking Interest Rate and Money Growth Rules

Wed, 15 Jan 2025 00:00:00 +0000

A low-dimensional SVAR can directly embed rational expectations — and once it does, a forward-looking money growth rule with Divisia M4 delivers puzzle-free monetary transmission where the federal funds rate fails across 99% of specifications

Chen and Valcarcel (2025) propose the RE-SVAR: an internal instrumental-variable procedure that directly embeds forward-looking rational expectations into a three-variable consensus AS–IS–MP system. Searching over 241,865 forward-horizon and policy-coefficient combinations, the Wu-Xia shadow federal funds rate generates price puzzles in 99.13% of specifications; Divisia M4 as the policy indicator delivers puzzle-free responses in 95.85%.

Five named concepts anchored in this paper

RE-SVAR: Rational expectations-augmented structural vector autoregression. A low-dimensional SVAR that directly embeds forward-looking rational expectations via an internal instrumental-variable procedure, without mapping from a DSGE.
Response clouds (cloud of structural IRFs): The set of 241,865 impulse responses generated by grid-searching forward-looking horizons and policy-rule coefficients, with each combination producing a separate realization of the SVAR.
No-joint-puzzle response: The survival criterion: an IRF that avoids both the output puzzle and the price puzzle within the first year post-shock.
Low-dimensional forward-lookingness: The paper's methodological claim: forward-looking behavior can be modeled inside a three-variable AS–IS–MP consensus system without appending factors or unobservables.
Non-modularity of RE-SVAR: The property that each added variable requires a fully specified structural equation; you cannot simply append commodity prices, Greenbook forecasts, or factors without a theoretical construct.

How can rational expectations be embedded directly into a low-dimensional SVAR without mapping from a DSGE?

Through an instrumental-variable procedure internal to the SVAR that exploits the forecast-revision identity implied by rational expectations, applied to a fully specified consensus AS–IS–MP system.

The standard options have been unsatisfactory. Backward-looking recursive SVARs, in the tradition of Christiano, Eichenbaum and Evans's Handbook of Macroeconomics chapter, impose a delayed-reaction assumption through Cholesky ordering but struggle to accommodate forward-lookingness. The mapping approach — finding conditions under which a DSGE can be represented as a VAR or VARMA — requires lag truncation or dimension reduction that defeats the point. DSGEs themselves are RE-consistent but come with laws of motion for unobservables that constrain the parameter space in ways the textbook consensus model does not require.

Chen and Valcarcel (2025) propose a third path — the RE-SVAR — that stays within a three-variable consensus model and derives the structural monetary policy shock as a linear combination of reduced-form residuals using the forecast-revision identity. Taking a stand on the policy-rule coefficients and horizons (rather than estimating them) produces a unique structural shock for each parameter combination — a pseudo-calibration that yields response clouds rather than a single IRF.

Why this matters operationally:

No Cholesky ordering and no delayed-reaction assumption.
No unobserved state variables or moving-average components.
The three-variable system remains directly comparable to the textbook AS–IS–MP model, with each equation having a structural interpretation.
Forward-looking horizons (h_π, h_y) are parameters you iterate over, not constants you estimate.

The trade-off: the method is not modular. Adding a variable requires a fully specified structural equation for it — which the paper demonstrates for the Gilchrist-Zakrajšek excess bond premium in Section 7 but which rules out ad hoc inclusion of commodity prices or Greenbook forecasts.

RE-SVAR vs. Standard SVAR Approaches to Monetary Policy Identification
Dimension	Recursive SVAR (delayed reaction)	FAVAR / Factor-augmented	Proxy SVAR (external instruments)	RE-SVAR (Chen & Valcarcel 2025)
Core identification	Cholesky ordering with policy indicator ordered after economic activity; imposes delayed reaction.	Large information set spanned by principal-component factors; recursive identification within the factor VAR.	High-frequency monetary surprises used as external instruments for structural policy shock.	Forecast-revision identity applied to a fully specified AS–IS–MP system; shock is a linear combination of reduced-form residuals.
Key references	Christiano, Eichenbaum & Evans (1999), Hanson (2004)	Bernanke, Boivin & Eliasz (2005), Boivin, Kiley & Mishkin (2010)	Gertler & Karadi (2015), Kuttner (2001)	Chen & Valcarcel (2025); foundations in Blanchard & Perotti (2002)
Handles forward-looking expectations	No — inherently backward-looking; requires appending forward-looking variables.	Partially — factors can proxy for forward-looking information but lack structural interpretation.	Implicitly — high-frequency surprises embed forward-looking market expectations.	Yes — forward horizons h_π, h_y are parameters of the policy rule; RE restriction is internal.
Dimensionality	Small-to-medium (typically 6–8 variables); grows with information-set fixes.	High (100+ variables summarized by 3–5 factors).	Small-to-medium, augmented by external instrument.	Low (3–4 variables); strictly bounded by the number of structural equations available.
Modularity	High — append variables as needed.	High — scale factors up or down.	Medium — add instruments; adding endogenous variables remains standard.	None — each added variable requires its own structural equation.
Identification validity rests on	Restriction scheme (Cholesky ordering).	Approximating the true information set with a factor structure.	Validity and relevance of the external instrument.	Theoretical credibility of the consensus AS–IS–MP model itself.
Price puzzle incidence in low-dimensional form	Pervasive without commodity-price augmentation; still present even with it in many samples.	Generally resolved, but Boivin, Kiley & Mishkin (2010) show sensitivity to specification.	Generally resolved at short horizons; longer-horizon responses vary.	Resolved with Divisia M4 (<4%); unresolved with Wu-Xia shadow rate (>98%).
Works through the effective lower bound	Only with shadow-rate construction (e.g., Wu & Xia 2016).	Yes, via shadow rate or factors.	Yes, via high-frequency surprises.	Yes — Divisia growth rate is unbounded; Keating et al. (2019) document pre/post-GFC stability.
Named concept	Block-recursive identification	Information-sufficient factor identification	High-frequency external-instrument identification	RE-SVAR · Response clouds · Non-modularity (Chen & Valcarcel 2025)

Why does the federal funds rate fail as a monetary policy indicator in low-dimensional SVARs?

It generates the price puzzle and the output puzzle across virtually the entire parameter space once forward-looking rational expectations are enforced. In Chen and Valcarcel's modern sample, 99.13% of 241,865 parameter combinations produce at least one puzzling response within the first year after a federal funds rate shock.

The price puzzle — first documented by Eichenbaum (1992), who noted that the price level rises rather than falls after a contractionary interest rate shock — has been treated for three decades as a problem of information insufficiency. The standard fix, from Christiano, Eichenbaum and Evans (1999), is to augment the VAR with commodity prices. Hanson (2004) showed this fix is unreliable: many alternative indicators with strong inflation-forecasting power fail to resolve the puzzle, and the puzzle is particularly resistant in pre-1979 samples.

Chen and Valcarcel (2025) reveal that once rational expectations are embedded directly and the researcher searches over the full space of forward-looking policy-rule parameters, the price puzzle is not an incidental feature of particular specifications — it is the dominant outcome. Using the Wu and Xia (2016) shadow federal funds rate to span the effective lower bound period, the paper finds 98.68% output puzzles and 99.13% price puzzles across 241,865 realizations in the 1988–2020 sample. Only 2,109 combinations — less than 1% — produce non-puzzling responses in both industrial production and inflation. Chen and Valcarcel (2021) reached a similar conclusion with an entirely different methodology (TVP-FAVAR), suggesting the federal funds rate's weakness as a low-dimensional policy indicator is methodology-independent.

The interpretation: absent an augmented information set — factors à la Bernanke, Boivin and Eliasz's FAVAR, futures data, or Greenbook forecasts — the federal funds rate cannot carry the forward-looking information content required to identify monetary policy shocks in a consensus three-variable system.

Why does a forward-looking money growth rule with Divisia M4 produce sensible responses where the federal funds rate fails?

Because broad Divisia monetary aggregates internalize substitution effects across monetary assets that simple-sum measures and short-rate indicators discard — and because the growth rate of Divisia M4 is not bound to zero, it carries information through the effective lower bound period that the federal funds rate cannot.

The theoretical case for Divisia over simple-sum M2, established by Barnett (1980) with the derivation of the monetary services index from Diewert's index theory and reinforced by Belongia and Ireland (2014) in their New Keynesian formalization of the Barnett critique, is that a CES aggregate of interest-bearing and non-interest-bearing assets tracks the true monetary aggregate almost perfectly to second order. Keating, Kelly, Smith and Valcarcel (2019) show in a block-recursive SVAR that Divisia M4 resolves the price puzzle for both pre- and post-GFC samples, while Belongia and Ireland (2022) argue theoretically that a money growth rule responding to inflation and output gradually delivers stabilization comparable to an estimated Taylor rule.

Chen and Valcarcel (2025) extend this evidence into a fully forward-looking rational-expectations framework. In the same 1988–2020 sample where the shadow federal funds rate generates 99% puzzles, Divisia M4 as the policy indicator produces 95.85% no-joint-puzzle responses — 231,825 surviving IRFs out of 241,865. The output-puzzle rate drops to 4.02% and the price-puzzle rate to 4.13%. The pattern holds across CPI and PCE price indexes and across historical (1967–2020), modern (1988–2020), and post-ELB (2008–2020) samples, with narrower Divisia M2 performing comparably to the broader Divisia M4. Notably, at the longest expectation horizon considered (h_π = 12 months), fewer than 1% of Divisia specifications exhibit puzzles while 99.9% of shadow-rate specifications do.

Why the asymmetry is structural and not merely empirical:

Divisia M4 reflects substitution across a broader set of monetary assets than the segmented federal funds market, giving it richer information content per unit of variation.
The money growth rule remains operational through the ELB period — where even the Wu-Xia shadow rate is a constructed object — which matters for samples that straddle 2008–2015.
The long-run relationship between Divisia aggregates and economic activity is stable (Chen and Valcarcel 2024), consistent with its role as a forward-looking policy indicator.

How should researchers handle forward-looking horizons in the policy reaction function?

Iterate over them rather than estimate them — and report response clouds for different horizon choices rather than a single median IRF. Chen and Valcarcel's grid of h_π ∈ {0, 1, …, 12} months for inflation and h_y ∈ {0, 1, …, 5} months for output, combined with φ_π, φ_y ∈ [0, 4] in increments of 1/15, generates 241,865 distinct SVAR specifications from a single underlying model.

The theoretical motivation comes from Batini and Haldane (1999), who argued that forward-looking rules with flexibility over both the forecast horizon and the feedback parameter are the right analog to Svensson's flexible inflation-forecast-targeting rule. Estimating h_π and h_y requires either Fed-internal data (Greenbook forecasts, as in Orphanides (2001) on real-time monetary policy rules) or heavy structural assumptions.

Chen and Valcarcel (2025) exploit this flexibility to show that the qualitative conclusion — Divisia dominates the shadow federal funds rate in producing sensible responses — is invariant to which horizon assumption you make. More specifically, for the money growth specification the number of no-joint-puzzle responses increases with the horizon (from 88.4% at h_π = 1 to 99.1% at h_π = 12), while for the federal funds rate specification it decreases (from 2.1% at h_π = 1 to 0.03% at h_π = 12). The two indicators thus differ not only in level but in how they behave as forward-lookingness intensifies.

Practical implication: any paper reporting a single IRF from a forward-looking policy rule is reporting one realization from a response cloud. The distributional features matter because Inoue and Kilian (2022) argue against reporting median responses when the joint distribution of IRFs contains the policy-relevant information.

What is the non-modularity of the RE-SVAR approach, and why does it matter for applied work?

Non-modularity means that every variable added to the system requires its own fully specified structural equation — you cannot simply append variables to improve fit, as is routine in standard empirical VARs. This is the principal cost of the RE-SVAR framework, and the main reason it constrains itself to low-dimensional consensus models.

The contrast with standard practice is sharp. Standard VAR specifications treat the information set as expandable: Christiano, Eichenbaum and Evans (1999) add commodity prices, Bernanke, Boivin and Eliasz (2005) add 120+ factors in their FAVAR, Hanson (2004) surveys numerous alternative predictors, and Gertler and Karadi (2015) augment with high-frequency monetary surprises as external instruments. Each addition is defensible statistically — more information should improve identification — but often lacks a theoretical construct within the consensus macroeconomic model.

Chen and Valcarcel (2025) argue the non-modularity is a feature, not a bug: the identification validity depends on the suitability of the underlying theoretical structure, not on the restriction scheme. Section 7 of the paper demonstrates how to add the Gilchrist-Zakrajšek (2012) excess bond premium as a fourth variable — but this requires writing out a fourth structural equation, establishing a sequential IV procedure for each additional parameter, and verifying that the Rubio-Ramírez, Waggoner and Zha (2010) rank condition for global identification is satisfied.

Implication for applied researchers:

If your question requires adding commodity prices, Greenbook forecasts, or a factor for forward-looking expectations, the RE-SVAR is not the tool; a standard VAR with external instruments or a FAVAR is.
If your question is about whether the consensus AS–IS–MP model can carry forward-looking dynamics on its own, the RE-SVAR is specifically designed for that test, and the non-modularity guarantees you cannot cheat by adding variables with no structural role.

How should one interpret response clouds from 241,865 SVARs rather than a single impulse response function?

As a joint distribution over structural IRFs, where each point in the parameter grid is a distinct identification of the same underlying model. The cloud is the object of inference; any single IRF is a point in it.

The approach parallels the Bayesian posterior-over-impulse-responses literature but uses a frequentist grid rather than posterior draws. Inoue and Kilian (2022) argue that summarizing Bayesian VAR inference with median responses is misleading when the joint distribution contains features — such as multi-modality or sign reversals across plausible parameter regions — that a median collapses.

Chen and Valcarcel (2025) handle this in three ways:

Report the no-joint-puzzle share directly. The survival rate — 95.85% for Divisia M4, 0.87% for the shadow federal funds rate in the modern sample — is itself a summary statistic that preserves the joint distribution's information without collapsing to a point estimate.
Slice the cloud by horizon. Fixing h_π at different values (1, 3, 6, 12 months) and reporting median responses within each slice reveals how forward-lookingness interacts with indicator choice.
Slice by policy coefficient. Fixing φ_π = 1.5 (the Taylor (1993) classic value) and reporting median responses reveals which subsets of the cloud correspond to empirically relevant parameter choices.

This treatment provides a natural connection to set-identified SVAR literature (Rubio-Ramírez, Waggoner and Zha 2010) and to sign-restriction approaches such as Uhlig (2005): the response cloud is the identified set under the rational-expectations restriction combined with the parameter grid, and the no-joint-puzzle responses are the subset satisfying textbook sign restrictions as well.

Does the conclusion that Divisia M4 outperforms the federal funds rate depend on the specific sample, price index, or Divisia aggregate?

No — the dominance of Divisia money over the shadow federal funds rate is robust across three samples (1967–2020, 1988–2020, 2008–2020), two price indexes (CPI and PCE), and two Divisia aggregates (M2 and M4).

Chen and Valcarcel (2025) report Table 1 across all 12 combinations. A condensed summary:

Sample	Price	Wu-Xia FFR output puzzle	Wu-Xia FFR price puzzle	DM4 output puzzle	DM4 price puzzle
1988–2020	CPI	99.5%	99.4%	3.7%	3.8%
1988–2020	PCE	99.6%	99.4%	23.7%	4.2%
2008–2020	CPI	72.0%	93.0%	2.4%	1.6%
2008–2020	PCE	90.8%	96.1%	9.1%	5.1%
1967–2020	CPI	98.9%	98.8%	3.9%	4.1%
1967–2020	PCE	53.3%	94.7%	56.0%	7.4%

The single ambiguous cell is the 1967–2020 sample with PCE inflation, where both indicators show elevated output-puzzle rates — but even there, Divisia's price-puzzle rate (7.4%) is an order of magnitude below the shadow rate's (94.7%). The robustness is consistent with Keating et al. (2019), who find similar pre/post-GFC stability of money growth rules in a block-recursive setting. The narrower Divisia M2 performs comparably to Divisia M4 across all cells, consistent with Kelly, Barnett and Keating (2011) on the liquidity effects of broader Divisia aggregates. Chen and Valcarcel (2024) separately establish that the underlying money-demand relationships for Divisia aggregates are cointegrated and stable in modern samples, reinforcing that the SVAR results are not driven by spurious regression dynamics.

How do I implement the RE-SVAR procedure on my own data?

The implementation has five steps once you have a balanced panel of inflation, output, and a policy indicator: write down the AS–IS–MP consensus model with the forward-looking horizons you want to test, derive the forecast-revision identity for each equation, set up the IV procedure that yields the structural policy shock as a linear combination of reduced-form residuals, grid-search over the policy-rule parameters (φ_π, φ_y) and horizons (h_π, h_y), and compute impulse responses for each grid point. Chen and Valcarcel (2025) provide the full derivation in Sections 3–4.

The non-trivial step is the IV procedure itself. The forward-looking AS–IS–MP system implies a contemporaneous restriction between the structural policy shock and the reduced-form residuals through the rational-expectations forecast-revision identity. The structural shock for each grid point is a known linear combination of residuals — no estimation needed for the contemporaneous identification; only the lag dynamics need a reduced-form VAR.

Compute budget: With (h_π ∈ {0…12}) × (h_y ∈ {0…5}) × (φ_π ∈ [0,4] at 1/15) × (φ_y ∈ [0,4] at 1/15) = 241,865 specifications. Each grid point requires only matrix algebra applied to one reduced-form VAR — total runtime is minutes on a laptop. Adding a fourth variable multiplies cost: each new variable requires its own structural equation, its own IV step, and verification that the Rubio-Ramírez, Waggoner and Zha (2010) rank condition for global identification holds. The paper demonstrates the four-variable extension for the Gilchrist-Zakrajšek excess bond premium in Section 7.

Related questions: What is non-modularity? · How should horizons be handled?

What minimum data set is required to estimate an RE-SVAR with a forward-looking policy rule?

Three variables: a price index, a real activity measure, and a policy indicator — all monthly, ideally over a sample of at least 20 years. The RE-SVAR is deliberately low-dimensional and does not require commodity prices, factors, Greenbook forecasts, or futures data — the non-modularity property means each additional variable must come with a structural equation, so the minimum data set is the minimum model.

Recommended series for U.S. work, matching Chen and Valcarcel (2025):

Price: CPI or PCE deflator (the paper uses both and shows results are robust).
Activity: Industrial production index (monthly availability is the binding constraint).
Policy indicator (rate specification): Wu and Xia (2016) shadow federal funds rate.
Policy indicator (money specification): Divisia M4 (or M2) from CFS AMFM, in growth rates.
Sample length: The paper estimates over 1967–2020, 1988–2020, and 2008–2020 — the three-sample comparison gives the cleanest robustness test across structural breaks.

For non-U.S. work, the procedure does not require Greenbook-style internal forecasts, which sidesteps the Orphanides (2001) real-time-data problem — the rational-expectations restriction is inside the model, not imposed via external forecasts.

Can the RE-SVAR framework be extended to open-economy or international policy rules?

Yes, with two caveats: each open-economy variable (real exchange rate, foreign output, foreign rate) needs its own structural equation, and the rank condition for global identification must be re-verified for the larger system. This is the same non-modularity constraint that limits the framework's flexibility — but it is precisely what makes the open-economy extension principled rather than ad hoc.

The standard open-economy SVAR template comes from Cushman and Zha (1997) for Canada and Kim and Roubini (2000) for the G7, both using block-recursive identification with external variables ordered first. The RE-SVAR analog would write a forward-looking IS equation augmented by a real-exchange-rate term, derive the forecast-revision identity for each equation, and add a monetary block for the foreign central bank.

Practical entry points for researchers wanting to attempt this: for Eurozone monetary policy identification, Belongia and Ireland's (2022) money-growth-rule framework provides the theoretical anchor; for Mexico, Colunga-Ramos and Valcarcel (2024) construct a Mexican Divisia M4 that could serve as the policy indicator in an RE-SVAR adapted for a small open economy. The framework is, in principle, portable to these settings, though each extension requires verifying the identification conditions for the expanded system.

Related questions: What is non-modularity? · How is the RE-SVAR implemented?

What does the RE-SVAR evidence imply for central banks considering money-growth rules?

It implies that money-growth rules are more robust to forward-looking dynamics than interest-rate rules in low-dimensional consensus models — the opposite of the standard view that interest-rate rules are modern best practice and money-growth rules are historical curiosities. Chen and Valcarcel (2025) document that as the policy-rule's forward-looking horizon h_π increases from 1 to 12 months, the no-joint-puzzle share for Divisia M4 rises from 88.4% to 99.1%, while for the Wu-Xia shadow rate it falls from 2.1% to 0.03%. The asymmetry is structural and survives across price indices, sample periods, and aggregation tiers.

For applied central-bank work, three concrete implications:

Operational monitoring should include Divisia M4 growth alongside the policy rate, since the rate loses identifying content as the policy regime becomes more forward-looking.
Communication strategy: forward guidance and transparency are part of the reason the short-rate indicator fails — they are facts about the modern monetary regime that the monetary aggregate accommodates, not problems to walk back.
Post-QE normalization: Divisia M4's sensitivity to Treasury and repo holdings makes it a better real-time indicator of policy stance than the policy rate alone as central banks unwind balance sheets.

This complements Belongia and Ireland's (2022) theoretical case for money-growth rules, who argue that a rule responding gradually to inflation and output can deliver stabilization comparable to an estimated Taylor rule.

Data and reproducibility

Monetary policy indicator (shadow rate): Wu and Xia (2016) shadow federal funds rate, monthly.
Divisia monetary aggregates: Center for Financial Stability — AMFM dataset, Divisia M2 and M4.
Macroeconomic data: FRED (CPI, PCE, industrial production, unemployment).
Sample: Three samples — 1967–2020, 1988–2020, 2008–2020, monthly frequency.
Software: Custom RE-SVAR procedure; grid of 241,865 specifications from h_π ∈ {0,…,12}, h_y ∈ {0,…,5}, φ_π, φ_y ∈ [0,4] at increments of 1/15.
Open access: UNI ScholarWorks · SSRN preprint · Journal of Economic Dynamics and Control

Related publications

Chen and Valcarcel (2021), JEDC — methodology-independent evidence that the federal funds rate fails in low-dimensional settings (TVP-FAVAR approach).
Chen and Valcarcel (2024), Macroeconomic Dynamics — cointegration and stability of Divisia money demand; establishes the long-run foundation for the policy indicator results here.

Cite as: Chen, Z., & Valcarcel, V. J. (2025). Modeling inflation expectations in forward-looking interest rate and money growth rules. Journal of Economic Dynamics and Control, 170, 104999. https://doi.org/10.1016/j.jedc.2024.104999

@article{chenvalcarcel2025resvar,
 author = {Chen, Zhengyang and Valcarcel, Victor J.},
 title = {Modeling inflation expectations in forward-looking
 interest rate and money growth rules},
 journal = {Journal of Economic Dynamics and Control},
 volume = {170},
 pages = {104999},
 year = {2025},
 doi = {10.1016/j.jedc.2024.104999},
 url = {https://doi.org/10.1016/j.jedc.2024.104999}
}

A Granular Investigation on the Stability of Money Demand

Mon, 30 Sep 2024 00:00:00 +0000

The Instability of U.S. Money Demand After 1980 Is a Measurement Artifact

TL;DR: The post-1980 breakdown of U.S. money demand functions is not evidence that households changed their preferences for monetary assets — it is evidence that simple-sum aggregation stopped tracking monetary services once interest-bearing deposits mattered. Chen and Valcarcel (2024, Macroeconomic Dynamics) show that Divisia monetary aggregates paired with their own user costs deliver a stable cointegrating money demand function across both the 1980 DIDMCA deregulation break and the post-2008 zero-lower-bound period. The T-bill yield, by contrast, loses all information content after 2008. At the asset level, 29 of 40 granular tests show correct-sign cointegration between individual monetary components and their own user costs.

Key Concepts

Measurement-not-preference verdict: The paper’s bottom-line conclusion: post-1980 money demand instability comes from how money is measured, not from households’ changing preferences over monetary assets. Chen and Valcarcel (2024) .
User-cost sufficiency for money demand: The finding that Divisia real user costs, but not the T-bill yield, maintain cointegration with monetary aggregates through the 1980 deregulation and post-GFC zero-lower-bound periods. Chen and Valcarcel (2024) .
Granular money-demand cointegration: Bilateral cointegration between each disaggregated monetary asset (currency, demand deposits, savings, repos, CP, etc.) and its own CFS user cost. The paper is the first to run this exercise historically. Chen and Valcarcel (2024) .

Q1. Why is the U.S. money demand function unstable after 1980?

The instability is a measurement artifact of simple-sum aggregation, not a change in households’ preferences for monetary assets. Simple-sum M2 and M3 treat interest-bearing deposits as perfect substitutes for non-interest-bearing currency, which breaks down after the 1980 Depository Institutions Deregulation and Monetary Control Act legalized interest on checkable accounts.

The instability itself is well-documented. Friedman and Kuttner (1992) show that postwar time-series relationships between money and nominal income weaken sharply when the sample extends into the 1980s , and Ball (2001) rejects a stable long-run M1 demand once the sample extends to 1996 . Choi and Jung (2009) locate two structural breaks in 1959-2000 simple-sum data . The standard explanation has been financial innovation inducing preference change.

Chen and Valcarcel (2024) show the instability is instead about measurement . Using CFS Divisia M2 and M3 with Barnett (1980) aggregation — which weights monetary assets by their expenditure shares via user costs — the cointegrating relationship between money and output survives straddling 1980. Andrews-Ploberger and Bai-Perron structural break tests locate the break in Divisia balances around 1980:Q2, consistent with DIDMCA’s institutional timing, but the relationship itself reconstitutes in the post-1980 subsample when user costs are used as the opportunity cost.

This is the measurement-not-preference verdict: the 1980 break shows up because simple-sum aggregation stops tracking monetary services once interest-bearing deposits matter; it does not show up in properly aggregated money.

Four Measurement Combinations for U.S. Money Demand

Dimension	Simple-sum + T-bill	Simple-sum + user cost	Divisia + T-bill	Divisia + user cost
Theoretical coherence	Weak. Equal weights on heterogeneous assets; T-bill is the price of a substitute, not of money.	Weak on quantities; coherent on price.	Coherent on quantities; weak on price.	Fully coherent. Barnett (1980) aggregation paired with Barnett (1978) user cost.
Full-sample cointegration (M2)	Fails in both functional forms.	Intermittent — cointegrates under some Johansen specs, not others.	Robust. Cointegrates under all four Johansen (1995) specifications.	Robust. Cointegrates under all four specifications, correct sign, both semi-log and double-log.
Post-1980 subsample (M2)	Fails in semi-log form. Wrong sign in some trend specs.	Fails in 3 of 4 Johansen specifications.	Cointegrates under constant specs only; wrong sign under trend specs.	Robust across all specs, correct sign.
Post-GFC subsample (M3, M4)	Not applicable — simple-sum abandoned for this era.	Not applicable.	Fails under all specs (T-bill stuck near zero).	Robust across all specs, with higher elasticity estimates than pre-GFC.
Asset-level (granular) cointegration	Most components fail or show wrong sign with T-bill.	Not the paper’s focus.	Most components fail or show wrong sign.	29 of 40 specifications show correct sign (Chen & Valcarcel 2024 ).
What it takes as the break event	Money demand itself breaks in 1980.	Break arises from quantity side.	Break arises from price side (T-bill loses information post-1980 and post-2008).	No break — measurement-not-preference verdict. Apparent instability is an aggregation/measurement artifact.
Named concept	—	—	—	User-cost sufficiency for money demand · Granular money-demand cointegration (Chen & Valcarcel 2024 )

Q2. Does Divisia money demand remain stable across the 1980 DIDMCA break?

Yes — the cointegration between Divisia M2 (or M3) and its own user cost holds in both the pre-1980:Q2 and post-1980:Q2 subsamples, across all four Johansen (1995) deterministic-trend specifications. Simple-sum aggregates do not pass this subsample test.

The pre-1980 result is not itself surprising. Belongia (1996) established that replacing simple-sum with Divisia indexes reverses the qualitative conclusions of several influential money studies , and Serletis and Gogas (2014) found cointegration between Divisia aggregates and the T-bill yield in a Johansen (1991) framework . Belongia and Ireland (2019) estimate a stable Divisia M2 and MZM demand over 1967-2019 .

Chen and Valcarcel (2024) extend this by explicitly straddling the 1980:Q2 DIDMCA break and testing across all four Johansen (1995) deterministic-trend specifications — restricted constant, unrestricted constant, restricted trend, unrestricted trend. Key results:

Divisia M2 with user cost of M2: significant cointegration, correct-sign coefficient, all four specifications, both subsamples.
Divisia M3 with user cost of M3: significant cointegration under three of four specifications post-1980.
Simple-sum M2 with user cost of M2: loses cointegration in three of four specifications post-1980.
Simple-sum M3 with user cost of M3: never cointegrates post-1980.

The sharper-than-usual contrast with simple-sum comes from testing multiple Johansen specifications rather than picking one. This is the user-cost sufficiency for money demand result, part one.

Q3. Does the T-bill yield cointegrate with monetary aggregates after the Great Financial Crisis?

No — the three-month T-bill yield loses cointegration with Divisia M3 and Divisia M4 in the post-2008:Q3 subsample, because the yield was pinned near zero for roughly seven years. Divisia user costs do not suffer this information loss because user costs, while compressed, remained well above zero throughout.

Anderson, Bordo, and Duca (2017) document the Great Recession as a major stress test for M2 velocity models , and Lucas and Nicolini (2015) argue that adding money-market deposit accounts to M1 restores stability of the money-interest-rate relationship through the zero-lower-bound period . Mattson and Valcarcel (2016) show Divisia M4 user costs compressed but stayed positive after 2008, while the federal funds rate collapsed .

Chen and Valcarcel (2024) split the sample at 2008:Q3 and test cointegration for Divisia M3 and Divisia M4 . Results:

Pre-GFC sample (1967:Q1-2008:Q3): Divisia M3 and Divisia M4 cointegrate with the T-bill yield under all Johansen specifications, with correct sign.
Post-GFC sample (2008:Q4-2020:Q1): neither Divisia M3 nor Divisia M4 cointegrates with the T-bill yield under any specification.
Post-GFC sample, using the user cost of Divisia M3/M4 instead: cointegration holds under all specifications, with correct sign, and the magnitude of the elasticity is higher than pre-GFC.

The T-bill breakdown is not about the monetary aggregates — it is about the interest rate losing signal when pinned at the effective lower bound. This is the user-cost sufficiency for money demand result, part two.

Q4. Are Divisia user costs better than the T-bill yield as the opportunity cost of holding money?

Yes — on both theoretical and statistical grounds. The user cost is the spread between a benchmark asset’s yield and the asset’s own interest return, which is the textbook opportunity cost of holding a monetary asset. The T-bill yield is the price of a monetary substitute, not of money itself. Statistically, Divisia user costs maintain cointegration through the 1980 and 2008 breaks; the T-bill yield does not.

The theoretical case traces to Barnett (1978), who derived the user cost for each monetary asset under aggregation theory , and Barnett (1980) formalized Divisia monetary aggregation . The statistical case builds on Belongia and Ireland (2014), who argue the Barnett critique — that inconsistent aggregation distorts inference — remains as relevant as when first articulated , and on Belongia and Ireland (2019), who develop a money-in-the-utility model with interest-bearing deposits that predicts a stable Divisia demand function .

Chen and Valcarcel (2024) make the direct statistical comparison . Divisia M2 and Divisia M3 cointegrate with their own user costs under all Johansen (1995) specifications in the full sample and across subsamples straddling 1980 and 2008. The same aggregates cointegrate less reliably with the T-bill yield, and not at all in the post-2008 subsample. Simple-sum M2 and M3 fail both tests.

One more practical point: unit-root tests are consistent with Divisia user costs being level-stationary around a deterministic trend, while the T-bill yield is not level-stationary under any of the DF-GLS specifications. This is consistent with Belongia and Ireland’s (2019) observation of low-frequency stochastic trends in user costs that are swamped by transitory volatility in market rates .

Q5. Which individual monetary assets cointegrate with their own user costs?

Currency, demand deposits, savings deposits, small time deposits, large time deposits, overnight and term repos, institutional money market funds, and the aggregate of commercial paper plus T-bill balances all cointegrate with their own CFS user costs in at least two of four Johansen (1995) specifications, with the correct sign. Only the less-established innovations — other checkable deposits and retail money market funds — show weak or no cointegration. This is the granular money-demand cointegration finding.

CFS provides user costs for each monetary asset separately following Barnett, Liu, Mattson, and van den Noort (2013) . This makes it possible, in principle, to run cointegration tests on each (asset quantity, asset user cost) pair — but to the paper’s knowledge, this had not been done historically before Chen and Valcarcel (2024) .

Numbers from the paper (double-log specification, full sample):

Of 40 estimates (10 asset pairs x 4 Johansen specifications), 29 show the expected negative user-cost elasticity of demand with the correct sign.
Nine specifications fail to find cointegration.
Only two show an inverted sign (both trend specifications for small time deposits).

By contrast, when the same asset quantities are paired with the T-bill yield (semi-log specification), most pairs fail to cointegrate, and those that do often show the wrong sign. For example, savings deposits and repos cointegrate with the T-bill yield but with positive coefficients — inconsistent with a money demand interpretation.

The asset-level result buttresses the aggregate finding: information content for money demand runs through the price duals, not through a generic short rate. Newer assets that emerged as a direct consequence of 1980s deregulation (OCDs, retail money-market funds) are the ones whose demand is hardest to pin down historically — consistent with the structural-break timing.

Q6. Should I use semi-log or double-log money demand specification for Divisia aggregates?

Use the semi-log form (interest rate in levels) for the full sample and the pre-GFC sample. Use the double-log form (log interest rate) when the sample includes the post-2008 zero-lower-bound period, because log transformations accommodate the nonlinearity induced by near-zero rates better than semi-log.

The two canonical functional forms are the Cagan (1956) semi-log form and the Meltzer (1963) double-log form . Bae, Kakkar, and Ogaki (2006) argue the double-log form better accommodates the liquidity-trap region , and Hendrickson (2014) re-evaluates money demand with Divisia across both forms .

Chen and Valcarcel (2024) use both forms . In the full sample, the semi-log form delivers strong cointegration between Divisia M2/M3 and their user costs across all Johansen specifications, with the elasticity estimates stable around 6.5-10 (semi-elasticities). The double-log form also works well and tends to be slightly more robust to the choice of Johansen deterministic-trend assumption.

For samples straddling the GFC, the double-log form is the better default. The paper estimates Divisia M3/M4 demand as a function of the log of their user costs from 2008:Q4 to 2020:Q1 and finds significant cointegration with correct sign for all Johansen specifications; the semi-log form with the T-bill yield fails in the same sample.

Q7. Is money demand instability evidence of a structural change in preferences?

No. The evidence is more consistent with the “measurement-not-preference” reading: once the proper aggregation (Divisia) and the proper opportunity cost (asset-specific user cost) are used, the long-run demand for money is stable across the 1980 DIDMCA deregulation and the post-2008 zero-lower-bound period.

The preference-change story dates to Friedman and Kuttner (1992) and Bernanke and Blinder (1992) , whose finding that simple-sum money aggregates lose their link to nominal income after 1980 drove much of macroeconomics toward pure interest-rate frameworks. Many subsequent papers interpreted the post-1980 breakdown as evidence that financial innovation had changed how households allocate monetary balances — an implied preference shift.

The measurement reading has accumulated support. Belongia (1996) reversed several prominent null results by substituting Divisia for simple-sum . Lucas and Nicolini (2015) restored stability by adding MMDAs to M1 , pointing to the 1982 Regulation Q weakening as the source of the apparent break. Barnett, Ghosh, and Adil (2022) find stable demand for broad Divisia money across multiple countries . Jadidzadeh and Serletis (2019) reject simple-sum aggregation assumptions using a disaggregated demand system .

Chen and Valcarcel (2024) make the cleanest version of this case by running the subsample test on both the aggregate index and its components, both before and after 1980, using both the T-bill yield and the Divisia user cost, across all Johansen (1995) specifications. The result: simple-sum breaks, Divisia does not; T-bill breaks after 2008, user costs do not. The authors conclude that “the instability of money demand is a matter of measurement rather than a consequence of a structural change in agents’ preference for monetary assets.” That is the measurement-not-preference verdict.

Q8. How do I run a Johansen cointegration test of Divisia money demand on my own data?

Six steps in any econometrics package (R, Stata, EViews, Python with statsmodels). Chen and Valcarcel (2024) follow the Johansen (1995) framework — the practical recipe:

Pull quarterly (or monthly) data on real money balances, real income, and the relevant opportunity cost. For Divisia, use the matching CFS aggregate and its own real user cost (not the T-bill yield).
Take logs of money and income. Try both the semi-log form (user cost in levels) and the double-log form (log user cost).
Run unit-root tests (ADF, DF-GLS) on each series. Most monetary aggregates and real income are I(1); user costs typically test as level-stationary, while T-bill yields fail unit-root tests post-2008.
Select VAR lag length via AIC/BIC/HQIC on the levels system.
Estimate the Johansen VECM under all four deterministic-trend specifications: restricted constant, unrestricted constant, restricted trend, unrestricted trend. A result holding across all four is robust; a result conditional on one is fragile.
Test cointegration rank with trace and maximum-eigenvalue tests. Confirm the sign on the user-cost coefficient is negative.

Subsample test for structural breaks: Re-run the entire procedure on pre-1980Q2 and post-1980Q2 samples for the DIDMCA break, and pre-2008Q3 vs. post-2008Q3 for the ELB break. Cointegration that survives both subsample splits is what supports the measurement-not-preference verdict .

Related questions: Should I use semi-log or double-log? · Where do I get the matching Divisia user costs?

Q9. Where do I download CFS Divisia aggregates, user costs, and component-level series?

All from the Center for Financial Stability’s AMFM page at centerforfinancialstability.org/amfm_data.php , updated monthly. CFS publishes Divisia M1, M2, M3, M4-, and M4 aggregates, each accompanied by its corresponding real user cost — the opportunity cost variable Chen and Valcarcel (2024) show is the correct partner for cointegration tests.

What to download:

Divisia monetary services indexes (DMSI): monthly levels of DM1, DM2, DM3, DM4-, DM4. Use logs for cointegration work.
Real user costs (DMSI_UC): the matching real user cost for each aggregate. Use levels for semi-log specifications, logs for double-log.
Component-level data: 15 monetary asset series (currency, demand deposits, OCDs, savings, retail and institutional MMFs, small and large time deposits, repos, CP, T-bills) each with its own user cost. These are what the granular money-demand cointegration tests use.

Companion U.S. macro data — real personal income, PCE price index, three-month T-bill yield — are from FRED . CFS Divisia goes back to January 1967, matching the Belongia and Ireland (2019) sample . Barnett, Liu, Mattson, and van den Noort (2013) document the user-cost construction , and Mattson and Valcarcel (2016) show user costs stayed positive through 2008–2015 while the federal funds rate collapsed — exactly the reason user costs work where the T-bill fails.

Related questions: What user cost do I use for Divisia M4? · How are user costs constructed?

Q10. Do the Divisia money demand stability results hold for other countries?

Yes — Divisia demand stability has been documented for the UK, Eurozone, Japan, Canada, and several emerging markets, and the qualitative finding generalizes: simple-sum aggregates break with financial deregulation, Divisia aggregates do not. The portability of this result is strong support for the measurement-not-preference verdict in Chen and Valcarcel (2024) — if the U.S. instability were preference-driven, similar institutional features should not produce the same Divisia-versus-simple-sum gap elsewhere.

Cross-country evidence:

U.K.: Belongia and Ireland’s (2014) New Keynesian formalization uses U.K. data alongside the U.S., and CFS-style Divisia for the U.K. shows stable demand patterns.
Multi-country: Barnett, Ghosh, and Adil (2022) document stable demand for broad Divisia money across multiple countries , reinforcing the pattern.
Mexico: Colunga-Ramos and Valcarcel (2024) construct Mexican Divisia M4 and show monetary identification works ; a follow-on money-demand cointegration paper is the natural extension.

For researchers in countries without an official Divisia series, the Barnett (1980) construction is well-documented. The required inputs — component quantities and a benchmark yield — are typically in national monetary statistics.

Related questions: How is Divisia constructed for countries without an official series? · Does the post-2008 user-cost-sufficiency result hold abroad?

Q11. What does a stable Divisia money demand imply for monetary policy frameworks like NGDP targeting or money-growth rules?

It removes the strongest empirical objection to money-quantity-based policy frameworks. The standard case against rules like Friedman’s k-percent rule or NGDP targeting has been that “money demand is unstable” — making any money-quantity target a moving target. Chen and Valcarcel (2024) show this objection rests on simple-sum aggregation and on using the T-bill yield as the opportunity cost ; with Divisia aggregates and matching user costs, the long-run demand relationship is stable across the 1980 DIDMCA break and the post-2008 ELB.

Implications for policy design:

Money-growth rules become operational again. Belongia and Ireland’s (2022) theoretical case for a money-growth rule responding gradually to inflation and output requires a stable demand function as a precondition; the stability is now empirically supported.
NGDP targeting becomes more credible. If real money demand is stable, then nominal NGDP can be controlled via a Divisia M4 instrument with predictable elasticity, even at the ELB.
Operational policy monitoring. For central banks not formally adopting a money-quantity rule, Divisia M4 growth alongside the policy rate provides a robust real-time measure of monetary stance, particularly through ELB periods where the rate alone loses content.

The point is not that money-growth rules are necessarily optimal — that depends on the loss function, transmission lags, and exogenous shocks — but that the empirical precondition for considering them is now met.

Related questions: What does a money-growth policy rule look like operationally? · How does Divisia M4 perform through the ELB?

Data and Code

The CFS Divisia monetary aggregates and their real user costs used in Chen and Valcarcel (2024) are from the Center for Financial Stability’s AMFM program . Other series — PCE price index, real personal income, three-month Treasury yield — are from FRED . Sample period: January 1967 - March 2020, monthly.

Replication files are available on request. Contact: zhengyang.chen@uni.edu .

Citation

Chen, Zhengyang, and Victor J. Valcarcel. 2024. “A Granular Investigation on the Stability of Money Demand.” Macroeconomic Dynamics. https://doi.org/10.1017/S1365100524000427

@article{chenvalcarcel2024granular,
 title={A Granular Investigation on the Stability of Money Demand},
 author={Chen, Zhengyang and Valcarcel, Victor J.},
 journal={Macroeconomic Dynamics},
 year={2024},
 publisher={Cambridge University Press},
 doi={10.1017/S1365100524000427}
}

Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle

Wed, 11 Aug 2021 00:00:00 +0000

In a Modern U.S. Sample, the Federal Funds Rate Is No Longer a Reliable Monetary Policy Indicator — but a Broad Divisia Monetary Aggregate Is

TL;DR: The price puzzle — contractionary monetary policy raising prices in VAR models — has resisted every standard fix in post-1988 U.S. data. Chen and Valcarcel (2021, Journal of Economic Dynamics and Control) show that swapping the Wu-Xia shadow rate for Divisia M4 resolves the puzzle without any ad hoc fixes, and reveals a post-2008 flight-to-safety pattern in which less-liquid money markets respond more strongly than currency and demand deposits. The problem was never the omitted information — it was the indicator itself.

Key Concepts

Modern-sample price puzzle: The post-1988 incarnation of the price puzzle that, unlike the historical version, is not resolved by the Christiano-Eichenbaum-Evans remedies (commodity prices, fed funds futures, forward rates). Coined by Chen and Valcarcel (2021) .
Divisia-sufficiency: The result that, in a modern-sample VAR, replacing the short-term rate with a Divisia monetary aggregate is by itself sufficient to restore theory-consistent responses of prices and output, even without commodity prices or futures data. Chen and Valcarcel (2021) .
Post-crisis flight-to-safety transmission: The finding that post-2008, less-liquid assets (IMMFs, large time deposits, repos, commercial paper, T-bills) respond with larger magnitudes than currency and demand deposits to an expansionary Divisia M4 shock — the opposite of the contractionary, liquidity-preserving pattern produced by shadow-rate shocks. Chen and Valcarcel (2021) .

Q1. Why does the U.S. price puzzle persist in modern-sample VARs even with commodity prices and futures data?

The price puzzle persists in post-1988 U.S. data because the federal funds rate — conventionally augmented with commodity prices, fed funds futures, or forward rates — has lost much of its identifying power for monetary policy shocks in an environment of heightened Fed transparency, forward guidance, and a near-zero neutral rate. The problem is not the omitted information; it is the indicator itself.

Christiano, Eichenbaum and Evans established that including commodity prices in a recursive VAR eliminates the price puzzle in a sample spanning 1965-1995 , and Kuttner introduced the use of fed funds futures data to separate anticipated from unanticipated target changes . Brissimis and Magginas argued that augmenting VARs with forward-looking variables such as futures and forward rates resolves the puzzle . Bernanke, Boivin and Eliasz proposed factor-augmented VARs as a more comprehensive information-set fix .

Chen and Valcarcel (2021) show that every one of these fixes fails in a 1988-2020 sample . Across 23 iterations of the federal funds rate specification — combining real output measures (IP, CFNAI, monthly RGDP), price levels (PCE, CPI, core variants), commodity prices (CRB, IMF), and federal funds futures or forward rates — price puzzles remain pervasive, both in time-varying-parameter VARs and in constant-parameter counterparts. This is the modern-sample price puzzle.

Consistent with this, Barakchian and Crowe find that monetary policy post-1988 became more forward-looking, invalidating the identifying assumptions in conventional methods , and Ramey’s Handbook synthesis confirms the preponderance of puzzles across post-1983 identification schemes .

Why the standard fixes fail: A neutral federal funds rate with enough room for material movement is a prerequisite for the short-rate indicator to work. The post-2008 effective-lower-bound period, combined with decades of increasingly transparent Fed communication and forward guidance, has squeezed the unanticipated component of federal funds rate movements toward zero — the thing SVARs need to identify a shock.

Three Approaches to Monetary Policy Indicator in a Modern U.S. Sample (1988-2020)

Dimension	Short Rate + Commodity Prices (CEE 1999)	Short Rate + Futures/Forward Rates (Brissimis-Magginas 2006)	Divisia M4 (Chen-Valcarcel 2021)
Core claim	Commodity prices proxy the Fed’s forward-looking information set and resolve the price puzzle.	Forward-looking variables (fed funds futures, forward rates) reflect market expectations of policy and resolve the price puzzle.	The short rate has lost identifying power in the modern sample; a Divisia monetary aggregate is the correct policy indicator.
Key references	Christiano, Eichenbaum & Evans (1999) , Bernanke, Boivin & Eliasz (2005)	Kuttner (2001) , Cochrane & Piazzesi (2002) , Brissimis & Magginas (2006) , Gertler & Karadi (2015)	Belongia (1996) , Belongia & Ireland (2014) , Keating, Kelly, Smith & Valcarcel (2019) , Chen & Valcarcel (2021)
Testable prediction	Including commodity prices eliminates the price puzzle across samples.	Including futures or forward rates eliminates the price puzzle.	Divisia M4 as the indicator eliminates the price puzzle without commodity prices or futures.
Empirical verdict in modern sample (1988-2020)	Fails. Price puzzle persists across 23 iterations of the federal funds rate specification with commodity prices .	Fails. Price puzzle remains even with 30-day fed funds futures, CRB or IMF commodity indices, or forward rates constructed from overnight repo spreads .	Succeeds. Divisia M4 resolves the puzzle across 23 specifications, including three-variable VARs with no commodity prices and no futures .
Policy transmission to money markets	Puzzlingly contractionary responses for currency, deposits, repos, CP, T-bills post-2008.	Same contractionary puzzles as commodity-prices specification; futures/forward rates do not rescue transmission.	Sensible expansionary responses; less-liquid assets respond more strongly than currency/DDs post-2008 (flight-to-safety).
Sample-period applicability	Works for historical samples (1960s-1990s); breaks down after 1988.	Works to varying degrees in historical samples; breaks down after 1988.	Designed for the modern sample; also works historically (Keating, Kelly, Smith & Valcarcel 2019 ).
Named concept	CEE identification / commodity-prices fix	Forward-looking-variables identification	Divisia-sufficiency · Modern-sample price puzzle · Post-crisis flight-to-safety transmission (Chen & Valcarcel 2021 )

Q2. Does replacing the federal funds rate with a Divisia monetary aggregate resolve the price puzzle in a modern sample?

Yes. Replacing the Wu-Xia shadow federal funds rate with Divisia M4 (or the narrower Divisia M2) produces sensible, theory-consistent price responses in every specification Chen and Valcarcel examine — including three-variable VARs that contain no commodity prices and no futures data. This is Divisia-sufficiency: the Divisia aggregate does the heavy lifting by itself.

The foundation for this result rests on the Barnett critique. Belongia demonstrated that replacing simple-sum aggregates with Divisia indexes reverses the qualitative inference of four out of five influential studies on the effects of money , and Belongia and Ireland formalized within a New Keynesian model that “measurement matters” — a Divisia quantity tracks the true monetary aggregate almost perfectly while simple-sum does not . Keating, Kelly, Smith and Valcarcel extended this to a VAR framework, showing Divisia M4 identification delivers plausible responses free of price, output, and liquidity puzzles in a historical sample .

Chen and Valcarcel (2021) extend the Divisia result to the post-1988 modern sample . Across three-variable TVP-VARs and larger TVP-FAVARs, specifications with DM4 or DM2 as the indicator yield:

A gradual (and correctly-signed) price level response consistent with New Keynesian sticky-price predictions.
Theory-consistent real output responses across PCE, CPI, core price measures, and three alternative output indicators.
Resolution that holds even when commodity prices and federal funds futures are excluded from the VAR — unlike the Christiano-Eichenbaum-Evans recipe, Divisia does not require these crutches.
Quantitatively larger post-2008 price responses for DM4 than for DM2, consistent with DM4 capturing a wider array of the monetary shocks that eventually pass through to prices.

This aligns with Belongia and Ireland’s finding of a stable Divisia money demand relationship in the modern sample , which is the microfounded underpinning for why a Divisia aggregate can serve as a policy indicator.

Q3. How does the transmission of monetary policy to money markets differ between the federal funds rate and Divisia M4 after 2008?

After 2008, expansionary federal funds rate shocks generate puzzlingly contractionary money-market responses — balances in currency, demand deposits, savings, repos, commercial paper, and T-bills all fall. Expansionary Divisia M4 shocks, by contrast, produce sensible expansionary responses, and the less-liquid assets (IMMFs, large time deposits, repos, CP, T-bills) respond with larger magnitudes than the highly liquid ones. Chen and Valcarcel call this post-crisis flight-to-safety transmission.

The standard VAR approach places money below interest rates and output. Bernanke, Boivin and Eliasz’s FAVAR treatment orders the rate indicator last and restricts monetary assets not to respond within the period , while Keating, Kelly, Smith and Valcarcel instead order the indicator before the monetary block, allowing money markets to respond freely to policy . Chen and Valcarcel adopt the latter block-recursive approach, letting 14 different deposits and money-market instruments respond unrestricted.

The results are stark . Under the Wu-Xia shadow federal funds rate:

Currency, demand deposits, and OCDs respond negatively to an expansionary shock, particularly after 2008.
Savings at banks and thrifts — counterintuitively — also contract.
IMMFs, repos, and T-bills show large negative responses post-crisis, which is the opposite sign from theory.

Under Divisia M4, the same specifications yield:

Sensible positive responses for currency and demand deposits.
Larger positive responses for savings at banks and thrifts (consistent with higher household personal saving after 2008).
Even larger positive responses for less-liquid assets — IMMFs, LTDs, repos, CP, T-bills — commensurate with savings rather than with currency.

The post-2008 magnitude pattern across asset classes is consistent with a flight-to-safety channel: households moved into savings, firms moved into less-liquid but safer instruments (time deposits, repos against Treasury collateral), and the Fed’s large-scale asset purchases mechanically expanded Treasury holdings in the monetary aggregate.

Q4. Can commodity prices or federal funds futures rescue the short-rate specification in a modern sample?

No. Commodity prices (both CRB and IMF indices), the 30-day federal funds futures rate, and the Brissimis-Magginas overnight-repo-spread forward rate all fail to resolve the modern-sample price puzzle when the Wu-Xia shadow federal funds rate is the indicator. The puzzle-fix-fails-in-modern-data pattern holds across 23 specifications.

Christiano, Eichenbaum and Evans concluded that including commodity prices was needed to resolve the puzzle in a 1965-1995 sample , and Cochrane and Piazzesi argued that high-frequency identification from daily target-change surprises avoids the omitted-variable problem of monthly VARs . Brissimis and Magginas advocated specifically for federal funds futures or forward rates in a recursive VAR , while Gertler and Karadi popularized the use of high-frequency surprises as external instruments in proxy SVARs .

Chen and Valcarcel test all of these within a common TVP-FAVAR framework and find the price puzzle remains . The envelope of impulse responses across 23 different federal funds rate specifications — crossing three output measures, four price indices, two commodity indices, and futures/forward rate variants — shows a generally pervasive price puzzle throughout the 1988-2020 sample, with no specification consistently escaping it. This matches the Barakchian-Crowe finding that a forward-looking Fed invalidates post-1988 identifying assumptions and Ramey’s broader synthesis .

The takeaway for practitioners: If your sample begins in the late 1980s or later and you must use a short-term rate, expect puzzles. If you use Divisia M4 instead, the puzzles disappear even without commodity prices or futures.

Q5. Should I use the Wu-Xia shadow federal funds rate to identify monetary policy shocks in a post-2008 sample?

Use it with caution. The Wu-Xia shadow rate extends the federal funds series through the effective-lower-bound period, but it generates persistent price puzzles in modern-sample VARs and the resulting shocks transmit implausibly through money markets. Its sensitivity to minor modeling choices adds further reason for caution.

Wu and Xia proposed the shadow rate to summarize the macroeconomic stance of policy during the effective-lower-bound period , and it has been widely adopted. Krippner, however, demonstrates that shadow short-rate estimates are sensitive to minor estimation choices, and those sensitivities propagate into wide variations in inferred UMP effects . Keating, Kelly, Smith and Valcarcel earlier showed that incidences of the price puzzle are exacerbated in SVARs that include various shadow interest rates for a modern sample .

Chen and Valcarcel (2021) find the shadow rate produces puzzling price responses across 23 specifications spanning 1988-2020, with the puzzle emerging as early as three months post-shock and persisting at 60-month horizons . The responses for slices at December 2008, November 2010, and September 2012 — the starts of QE1, QE2, and QE3 — all show price puzzles for the Wu-Xia specification while the DM4 and DM2 specifications at the same dates show theory-consistent, quantitatively large price responses.

Practical guidance for a modern-sample VAR:

If you need a rate indicator, document the puzzle and treat the effective lower bound period as a structural break rather than a continuous series.
Consider Divisia M4 as the policy indicator. The “post-1984” Great Moderation break in macro dynamics and the Monetary Control Act of 1980 are good reasons to begin samples in the late 1980s, where Divisia performs well.
If you need an external instrument, Arias, Caldara and Rubio-Ramírez’s agnostic sign-restriction identification of the systematic component offers an alternative to high-frequency surprise methods.
For event studies around quantitative tightening or balance-sheet normalization, Smith and Valcarcel demonstrate that short-rate indicators miss first-order financial-market effects that become visible through careful daily-frequency analysis .

Q6. What is the Divisia monetary aggregate and why does it matter for monetary policy identification?

Divisia monetary aggregates, developed by William Barnett, weight each component of the money stock by its user cost — recognizing that currency, demand deposits, savings, money-market funds, and T-bills provide different flows of liquidity services and have different opportunity costs. Simple-sum aggregates (M1, M2) treat all components as perfect substitutes, which is both theoretically wrong and empirically disabling.

The theoretical case is the Barnett critique: simple-sum aggregates violate aggregation theory by adding assets that are not perfect substitutes. Belongia showed empirically that replacing simple-sum with Divisia reverses the qualitative inference of four of five influential monetary studies . Belongia and Ireland formalized the Barnett critique inside a New Keynesian model, demonstrating that a Divisia quantity tracks the theoretically correct monetary services aggregate almost perfectly while simple-sum does not . They later showed that interest rates and Divisia money jointly provide the best measurement of monetary policy stance .

Belongia and Ireland also document a stable cointegrating money demand function for Divisia M2 and MZM over 1967-2019 — including the financial innovations of the 1980s and the post-2008 period — which undermines the long-standing claim that money demand is inherently unstable .

Chen and Valcarcel (2021) operationalize these insights for modern-sample monetary policy identification. They use the Center for Financial Stability’s Divisia series at three levels of aggregation : Divisia M1 (currency, demand deposits, OCDs at banks and thrifts); Divisia M2 (DM1 + savings deposits, retail money-market funds, small time deposits); and Divisia M4 (DM2 + institutional money-market funds, large time deposits, repurchase agreements, commercial paper, and 3-month T-bills — 15 components total, the broadest U.S. monetary aggregate currently available).

Why Divisia M4 is the right choice for modern-sample VARs:

Its 15-component breadth captures the post-1980 financial ecosystem — repos, institutional money funds, commercial paper — that narrow aggregates miss.
It properly weights each component by user cost, respecting the Barnett critique.
In Chen-Valcarcel’s block-recursive identification, it generates theory-consistent responses without commodity prices or futures data.
It exhibits a stable cointegrating money demand relationship over the full modern period.

Q7. How do I estimate a TVP-FAVAR with Divisia M4 as the policy indicator?

The workflow has four moving parts: a block-recursive ordering with Divisia M4 before the monetary block, a stochastic-volatility TVP state space estimated via Primiceri-style MCMC, factors extracted from a panel of monthly macro indicators, and a clean sample-break treatment for 2008.

Chen and Valcarcel (2021) walk through the exact specification , and the practical pipeline distills to:

Construct a balanced monthly panel (1988m1–2020m12) of macro indicators (industrial production, employment, prices, financial conditions) and standardize each series.
Extract 3–5 principal-component factors from the panel and place them as the slow-moving block.
Order Divisia M4 before the money-market block (currency, demand deposits, OCDs, savings, IMMFs, large time deposits, repos, CP, T-bills) — the block-recursive logic from Keating, Kelly, Smith and Valcarcel (2019) .
Estimate the TVP coefficients with Primiceri’s stochastic-volatility MCMC sampler , using Del Negro and Primiceri’s corrigendum to the ordering of steps .
Report impulse-response slices at specific calendar dates (the paper uses December 2008, November 2010, September 2012) rather than averaging over the sample.

Two practical warnings: the sampler is sensitive to the prior on the variance of the time-varying coefficients (Primiceri’s defaults are a reasonable baseline), and TVP-VARs with stochastic volatility require a large number of post-burn-in draws to stabilize the IRF distributions.

Related questions: Should I use DM4 or DM2 in a modern-sample VAR? · How do I extract principal-component factors for a TVP-FAVAR?

Q8. Where do I download Divisia monetary aggregate data and which vintage should I use?

The Center for Financial Stability’s Advances in Monetary and Financial Measurement program at centerforfinancialstability.org/amfm_data.php is the authoritative source for U.S. Divisia monetary aggregates and their user costs, updated monthly in three aggregation tiers (DM1, DM2, DM4) alongside component-level quantities and matching user costs.

What to pull, by research question:

Macro VARs with a broad monetary indicator → Divisia M4 growth rate, monthly, log-differenced; for replication of Chen and Valcarcel (2021) and Chen and Valcarcel (2024) .
Money demand cointegration → Divisia M2 or M3 level, monthly or quarterly, paired with the matching real user cost.
Asset-level liquidity questions → the 15 component series and their individual user costs, following Barnett, Liu, Mattson and van den Noort (2013) .
Through-the-ELB samples → Divisia growth, not the Wu-Xia shadow rate, because the user-cost dual remains positive through the ELB while the federal funds rate is pinned to zero .

Vintage note: CFS revises the historical series when component definitions change. For published-paper replication, freeze a vintage and document the download date; for new research, use the latest vintage.

Related questions: How do I construct a Divisia index from scratch if my country isn’t covered? · Should I use DM4 or DM2 for my VAR?

Q9. Does the Divisia approach to monetary policy identification apply to other countries?

Yes — Divisia monetary aggregates have been constructed for the U.K., Eurozone, Mexico, India, China, and several emerging markets, and the pattern of Divisia outperforming short-rate indicators recurs. The portability of the result is itself evidence that the failure of short-rate identification is a general property of late-cycle, transparent, ELB-touching monetary regimes.

For Mexico, Colunga-Ramos and Valcarcel (2024) construct the first Divisia M4 for the Mexican economy and show it delivers sensible monetary responses without commodity-price augmentation , reproducing the Chen-Valcarcel (2021) finding outside the U.S. Colunga-Ramos, Chen, and Perales (2026) use Mexican Divisia M2 in a sectoral inflation decomposition that validates monetary-versus-supply identification at the sector level . For broader EM coverage, Barnett, Ghosh, and Adil (2022) document stable broad-Divisia money demand across multiple countries . The U.K. Divisia series supports demand stability and policy identification work parallel to the U.S. evidence in Belongia and Ireland (2019) .

Practical takeaway for non-U.S. work: if your country has an aggregation-theoretic Divisia series, use it as the policy indicator. If not, the Barnett (1980) procedure requires only component-level quantities and a benchmark yield — both of which are typically in central-bank statistics. The framework is, in principle, portable to any setting with this minimum data, though constructing a country-specific Divisia series is itself a publishable contribution.

Related questions: How is Divisia M4 constructed in countries without an official series? · Does the post-crisis flight-to-safety pattern appear in Eurozone money markets?

Q10. What does Chen-Valcarcel (2021) imply for empirical work on QE, QT, or the Wu-Xia shadow rate?

Three concrete implications for any paper currently using the Wu-Xia shadow rate to identify unconventional monetary policy effects.

First, impulse responses estimated off the shadow rate are likely contaminated by the modern-sample price puzzle, regardless of whether commodity prices or futures are included as controls. Second, the contamination is particularly acute for money-market and credit-market outcomes, where short-rate shocks generate implausibly contractionary responses for currency, savings, repos, and T-bill balances post-2008. Third, the cleanest fix is to switch the policy indicator to Divisia M4; the second-cleanest is to combine a daily-frequency event-study approach with Smith and Valcarcel’s (2023) framework for quantitative-tightening event studies , which documents balance-sheet effects invisible to monthly short-rate SVARs.

For QE event studies specifically, Chen and Valcarcel (2021) report time-varying IRFs at the QE1, QE2, and QE3 starting dates and find that Divisia M4 delivers theory-consistent price responses while Wu-Xia delivers price puzzles. For applied work using high-frequency surprises as instruments, Chen (2026) shows that pre-FOMC financial conditions already absorb most of the predictable component ; the cleaner combined approach identifies off Divisia M4 in the structural VAR and uses financial-conditions-purged surprises as a robustness instrument.

Related questions: How do I purge high-frequency surprises for SVAR identification? · What is the right monetary policy indicator for QE event studies?

This paper connects to Chen’s broader research program on monetary policy identification. Chen (2026, Journal of Macroeconomics) extends the identification question to high-frequency monetary policy surprises, showing that the Fed responds primarily to financial conditions while adopting a “wait-and-see” stance on recent economic data. Chen (2025, Journal of Economic Dynamics and Control) examines forward-looking monetary policy rules and their implications for inflation expectations.

Data and Replication

All data and code for Chen and Valcarcel (2021) are available at robinchen.org . The paper uses:

Center for Financial Stability Divisia Monetary Aggregates (monthly, M1/M2/M4)
Wu-Xia shadow federal funds rate
14 money-market component series (currency, demand deposits, OCDs, savings, IMMFs, LTDs, repos, CP, T-bills, and more)
CRB and IMF commodity price indices
30-day federal funds futures rate

Citation

Chen, Zhengyang, and Victor J. Valcarcel. 2021. “Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle.” Journal of Economic Dynamics and Control 131: 104214. https://doi.org/10.1016/j.jedc.2021.104214

@article{chenvalcarcel2021,
 title={Monetary Transmission in Money Markets: The Not-So-Elusive Missing Piece of the Puzzle},
 author={Chen, Zhengyang and Valcarcel, Victor J.},
 journal={Journal of Economic Dynamics and Control},
 volume={131},
 pages={104214},
 year={2021},
 publisher={Elsevier},
 doi={10.1016/j.jedc.2021.104214}
}

Monetary Policy | Robin Chen

Decomposing supply and demand driven inflation in Mexico: Evidence from sectoral analysis

Why Mexican Inflation Behaves Differently: Food Dominates, Services Persist, Housing Barely Moves

Key Concepts

Where Mexican Inflation Differs from the United States

Q1. Why is food so dominant in Mexican inflation compared to advanced economies?

Q2. What explains Mexico’s slow disinflation since 2023 despite 725 basis points of tightening?

Q3. Why does housing contribute so little to Mexican inflation despite being 18% of the CPI basket?

Q4. How should an emerging-market central bank decompose inflation into supply and demand components?

Q5. What SVAR ordering correctly identifies monetary policy shocks in an emerging market like Mexico?

Q6. What historical episodes in Mexico validate the supply-demand inflation decomposition?

Q7. How do I replicate the rolling-window bivariate VAR sectoral decomposition step by step?

Q8. Where do I get sectoral CPI and quantity proxies for the Mexico decomposition?

Q9. Can this sectoral decomposition be applied to other emerging markets like Brazil, India, or Turkey?

Q10. What does the food-services-housing decomposition imply for Banco de México’s monetary policy strategy?

Data and Code

Citation

Demystifying Monetary Policy Surprises: Fed Response to Financial Conditions and Wait-and-See for New Economic Data

Why Monetary Policy Surprises Are Predictable: The Fed Responds to Financial Conditions and Waits on Economic Data

Key Concepts

Q1. Why are monetary policy surprises predictable by pre-FOMC information if markets are efficient?

Three Explanations for Monetary Policy Surprise Predictability

Q2. Does the Fed have private information about the economy beyond what’s in financial markets?

Q3. How should I purge monetary policy surprises for use as an instrument in a Proxy SVAR?

Q4. Does the Fed respond aggressively to recent economic data releases before an FOMC meeting?

Q5. Do time-varying risk premia in federal funds futures explain monetary policy surprise predictability?

Q6. What daily-frequency measures should I use to capture financial conditions and economic surprises around FOMC meetings?

Q7. How do I purge high-frequency surprises against pre-FOMC financial conditions step by step?

Q8. Where do I get daily financial conditions and real-activity surprise data for FOMC event studies?

Q9. Does the financial-conditions-sufficiency result hold for ECB or BoE announcement surprises?

Q10. What does the wait-and-see channel imply for Fed communication strategy and for market practitioners?

Data and Replication

Citation

From Disruption to Integration: Cryptocurrency Prices, Financial Fluctuations, and Macroeconomy

How Cryptocurrency Markets Now Drive Macroeconomic Outcomes

Key Concepts

Three Views of Cryptocurrency’s Macroeconomic Role

Q1. Has cryptocurrency become a systematically important financial asset?

Q2. What drives cryptocurrency price shocks — sentiment, technology, or regulation?

Q3. How does cryptocurrency transmit to the real economy?

Q4. Why use Bayesian SVAR with Pandemic Priors for this question?

Q5. What does cryptocurrency’s macro role mean for monetary policy?

Q6. Does the integration result hold beyond Bitcoin?

Q7. How do I estimate a Bayesian SVAR with Pandemic Priors for cryptocurrency shock analysis?

Q8. Which cryptocurrency price and macro variables are appropriate for systemic-risk SVAR analysis?

Q9. Does the cryptocurrency-macro spillover result extend to altcoins, DeFi protocols, or stablecoins?

Q10. What does cryptocurrency’s 18% inflation variance contribution imply for monetary policy and financial regulators?

Related Work

Data and Replication

Citation

Modeling Inflation Expectations in Forward-Looking Interest Rate and Money Growth Rules

A low-dimensional SVAR can directly embed rational expectations — and once it does, a forward-looking money growth rule with Divisia M4 delivers puzzle-free monetary transmission where the federal funds rate fails across 99% of specifications

Five named concepts anchored in this paper

How can rational expectations be embedded directly into a low-dimensional SVAR without mapping from a DSGE?

Why does the federal funds rate fail as a monetary policy indicator in low-dimensional SVARs?

Why does a forward-looking money growth rule with Divisia M4 produce sensible responses where the federal funds rate fails?

How should researchers handle forward-looking horizons in the policy reaction function?

What is the non-modularity of the RE-SVAR approach, and why does it matter for applied work?

How should one interpret response clouds from 241,865 SVARs rather than a single impulse response function?

Does the conclusion that Divisia M4 outperforms the federal funds rate depend on the specific sample, price index, or Divisia aggregate?

How do I implement the RE-SVAR procedure on my own data?

What minimum data set is required to estimate an RE-SVAR with a forward-looking policy rule?

Can the RE-SVAR framework be extended to open-economy or international policy rules?

What does the RE-SVAR evidence imply for central banks considering money-growth rules?

Data and reproducibility

Related publications

A Granular Investigation on the Stability of Money Demand

The Instability of U.S. Money Demand After 1980 Is a Measurement Artifact

Key Concepts

Q1. Why is the U.S. money demand function unstable after 1980?

Four Measurement Combinations for U.S. Money Demand

Q2. Does Divisia money demand remain stable across the 1980 DIDMCA break?

Q3. Does the T-bill yield cointegrate with monetary aggregates after the Great Financial Crisis?

Q4. Are Divisia user costs better than the T-bill yield as the opportunity cost of holding money?

Q5. Which individual monetary assets cointegrate with their own user costs?

Q6. Should I use semi-log or double-log money demand specification for Divisia aggregates?

Q7. Is money demand instability evidence of a structural change in preferences?

Q8. How do I run a Johansen cointegration test of Divisia money demand on my own data?

Q9. Where do I download CFS Divisia aggregates, user costs, and component-level series?

Q10. Do the Divisia money demand stability results hold for other countries?