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

Sentiment and technology — not regulation or monetary policy. Chen (2025) classifies 67 major crypto-market events 2014-2023 into six categories and finds only sentiment (coefficient 1.36, t = 3.15) and technology (coefficient 1.02, t = 2.06) significantly explain the identified structural crypto shocks. Regulatory, monetary, infrastructure, and network-effect shocks are statistically insignificant. The narrative identification follows Romer and Romer (2004). Sentiment dominance validates Baker and Wurgler (2007), while the significant technology coefficient shows crypto is not pure speculation. This partially contradicts regulation-focused studies including Borri and Shakhnov (2020) and Chokor and Alfieri (2021).

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.

Bayesian SVAR | Robin Chen

Q: How do cryptocurrency price shocks transmit to financial markets and the real economy?

Cryptocurrency shocks now transmit 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. The scale and sign pattern is consistent with cryptocurrencies behaving as systematic risk-appetite amplifiers, not diversifiers, aligning with portfolio-theoretic predictions from Markowitz and CAPM and behavioral extensions from Baker and Wurgler (2007).

Q: How much of financial-market volatility is now driven by cryptocurrency shocks?

Chen (2025) finds that cryptocurrency 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 overturns the early-literature diversification claims in Bouri et al. (2017) and Charfeddine, Benlagha, and Maouchi (2020): in the 2015-2024 institutional-adoption era, cryptocurrencies are systematic risk amplifiers, not diversifiers. The empirical fingerprint — Financial Stress Index drops on impact then recovers — is consistent with a risk-on channel through intermediary balance sheets described by Adrian and Shin (2010) and Brunnermeier and Pedersen (2009).

Q: Do cryptocurrency shocks cause persistent inflation?

Yes. Chen (2025) shows crypto shocks explain 18% of long-horizon PCE price-level forecast-error variance and produce a persistent 0.15% rise in the price level — a signature of demand-driven inflation rather than transitory financial noise. The contribution rises from 3.6% at 6 months to 17.6% at 30 months, while S&P 500, CRB, and FSI shocks combined contribute 10.1% at 30 months. The mechanism fits New Keynesian demand-side transmission via the wealth channel (Case, Quigley, and Shiller 2005) and financial-accelerator channel. Divisia M4 shows contractionary response but insufficient to offset the price effect, suggesting monetary policy has been accommodative to crypto-driven inflation. Policy implication: central banks should incorporate cryptocurrency developments into inflation forecasts.

Q: How do you estimate a crypto-to-macro VAR cleanly through the COVID-19 period?

Use Pandemic Priors. Cascaldi-Garcia (2022) proposes extending the Minnesota prior with time dummies for the pandemic period, controlled by a hyperparameter φ. As φ → 0 pandemic observations are treated as exceptional; as φ → ∞ the setup reverts to conventional Minnesota priors. Chen (2025) selects φ = 0.1 by marginal-likelihood maximization over a grid from 0.001 to 500, using the dummy-observation implementation of Bańbura, Giannone, and Reichlin (2010). Setting φ = 500 (Minnesota limit) materially changes real-economy impulse responses — less persistent unemployment declines, less persistent industrial-production responses, more contractionary DM4 — confirming Pandemic Priors are necessary for this sample. Main findings are robust to alternative orderings, CPI vs PCE, and alternative financial-stress measures including the Gilchrist-Zakrajšek excess bond premium.

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

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

Cryptocurrency is now a macroeconomic asset: Bitcoin shocks drive 18% of long-run inflation and 27% of commodity-price variance

Cryptocurrency has crossed the threshold from speculative curiosity to systemically-integrated asset class. Chen (2025) uses a Bayesian structural VAR with Pandemic Priors over 2015–2024 to show that positive Bitcoin price shocks raise equity and commodity prices, ease financial stress, stimulate industrial production, and generate persistent demand-side inflation — with sentiment and technology identified as the dominant sources of exogenous crypto innovations.

Three named concepts anchored in this paper

Sentiment-financial linkage: The channel through which crypto price innovations propagate to equity and commodity markets by shifting aggregate risk appetite, rather than through fundamentals or diversification relationships.
Technology-real linkage: The delayed but persistent transmission of crypto shocks to industrial production and unemployment via investment-timing and real-options effects on technology-sector capital formation.
Dual-channel crypto transmission: The combined framework in which sentiment drives financial-market integration while technology drives real-economy transmission — replacing single-factor views that treat cryptocurrency as either purely speculative or purely fundamental.

How do cryptocurrency price shocks transmit to financial markets and the real economy?

Cryptocurrency shocks now transmit 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. This scale and sign pattern is consistent with cryptocurrencies behaving as systematic risk-appetite amplifiers, not as diversifiers.

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 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's work on financial shocks and uncertainty-channel mechanics from Bloom's uncertainty-shock framework, where asset-price volatility creates real-options effects on investment timing.

Three empirical signatures distinguish this 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: 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.

Three views of cryptocurrency's role in the financial-macro system
Dimension	Pure speculation / isolated asset	Safe haven / diversifier	Integrated risk-amplifier (Chen 2025)
Core claim	Crypto prices reflect speculation only; limited real economic content.	Crypto provides diversification and hedging against other asset classes or uncertainty.	Crypto is systemically integrated; shocks transmit to financial markets via risk appetite and to the real economy via investment and wealth channels.
Key references	Early speculative-bubble views; implicit in efficient-markets critiques.	Bouri et al. (2017), Charfeddine et al. (2020)	Chen (2025), grounded in Baker and Wurgler (2007), Jermann and Quadrini (2012)
Testable prediction	Crypto shocks should not systematically move other asset classes.	Crypto should show low or negative correlation with risk assets, especially in crises.	Crypto shocks should positively comove with equities and commodities, ease financial stress on impact, and generate lagged real-economy responses.
Empirical verdict	Rejected. Crypto shocks explain 17.7% of S&P 500 variance and 27.2% of CRB commodity variance in Chen (2025).	Rejected for the 2015–2024 sample. The contemporaneous rise in equities and commodities after a positive crypto shock is inconsistent with diversification.	Supported. Chen (2025) finds the predicted sign and magnitude pattern, with narrative validation confirming sentiment and technology as exogenous crypto-shock drivers.
Real-economy prediction	No transmission expected.	Weak or no transmission — crypto is "outside" the real economy.	Delayed positive output response, persistent unemployment decline, and persistent demand-driven inflation. Quantitatively modest (6.2%, 3.8% variance shares) but statistically robust.
Inflation prediction	None.	None or mild disinflationary (if crypto acts as a hedge).	Substantial: 18% of long-horizon price-level variance, persistent 0.15% PCE rise — demand-side transmission.
Policy implication	Central banks can ignore crypto.	Central banks can ignore crypto; regulators focus on fraud/AML.	Monetary authorities should incorporate crypto in inflation forecasts; financial regulators should monitor crypto as a systemic risk source.
Named concept	—	Portfolio diversification	Sentiment-financial linkage · Technology-real linkage · Dual-channel crypto transmission (Chen 2025)

How much of financial-market volatility is now driven by cryptocurrency shocks?

Cryptocurrency shocks now account for 17.7% of S&P 500 forecast-error variance at 6 months and 27.2% of CRB commodity variance at 30 months — putting crypto alongside traditional macro shocks as a first-order driver of financial-market fluctuations. Chen (2025) reports that crypto shocks explain 87.7% of cryptocurrency's own 6-month forecast-error variance, 17.7% of equity variance, 9.3% of commodity variance at 6 months rising to 27.2% at 30 months, and 5.7% rising to 8.2% of the Financial Stress Index.

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).

Mechanism. 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's liquidity-and-leverage work, Brunnermeier and Pedersen's market-liquidity model, and He and Krishnamurthy's intermediary asset-pricing framework.

Do cryptocurrency shocks cause persistent inflation?

Yes — and the effect is large. Crypto shocks explain 18% of long-horizon (30-month) price-level forecast-error variance and produce a persistent 0.15% rise in the PCE price index, a signature of demand-driven inflation rather than transitory financial noise. Chen (2025) finds the contribution rises from 3.6% at 6 months to 7.6% at 12 months to 17.6% at 30 months, while innovations in the S&P 500, CRB commodity index, and Financial Stress Index combined contribute 10.1% at 30 months. Crypto is the largest single non-own driver of price-level variance in this sample.

Mechanism. The pattern 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. The wealth channel ( Case, Quigley, and Shiller (2005) show wealth effects are strongest for assets perceived as permanent stores of value) and the financial-accelerator channel (Bernanke, Gertler, and Gilchrist 1999) both operate to amplify the inflationary impulse.

Monetary-policy response. Divisia M4 shows initial expansion followed by contraction after a positive crypto shock — evidence of endogenous 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.

Policy takeaway. Monetary authorities should incorporate cryptocurrency developments in inflation forecasting. The 18% long-horizon variance contribution is too large to treat as an afterthought — and the demand-driven nature of the impulse means it is policy-actionable, unlike a transitory financial-market shock.

What actually drives cryptocurrency price shocks? Is it regulation, sentiment, or technology?

Sentiment and technology — not regulation or monetary policy. Chen (2025) classifies 67 major crypto-market events from 2014–2023 into six categories and finds that 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.

Event-category setup. 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).

Why sentiment dominates. The result validates Baker and Wurgler's (2007) investor-sentiment framework — retail-dominated asset markets exhibit amplified price movements beyond fundamentals. It contradicts strong-form efficient-markets interpretations of crypto pricing. It also partially contradicts papers like Borri and Shakhnov (2020) and Chokor and Alfieri (2021), which emphasize regulation as a primary driver: Chen (2025) finds regulatory event dummies are statistically insignificant after controlling for the full SVAR system, suggesting regulatory effects are already captured by the contemporaneous reactions of other variables.

Why technology matters too. The significant technology coefficient establishes that cryptocurrency is not a pure speculative bubble — protocol upgrades and technical improvements generate measurable economic value, and Caporale, Gil-Alana, and Plastun (2018) earlier documented persistence in the cryptocurrency market consistent with technology-based fundamentals. Demir et al. (2018) find economic-policy uncertainty predicts Bitcoin returns in ways consistent with a hedging demand driven partly by underlying protocol properties.

Why are crypto shocks strongly inflationary but only modestly expansionary for output and employment?

Because the financial-market channel is fast and wide while the real-economy channel is slow and narrow. Chen (2025) finds crypto shocks contribute 17.7% to S&P 500 variance and 27.2% to commodity variance, but only 6.2% to industrial production variance and 3.8% to unemployment variance at 30 months. The output response is a delayed 0.15% rise in industrial production — real, but small relative to the financial-market response.

The asymmetry reflects how the two channels work. The financial-market response operates through portfolio rebalancing and risk-appetite shifts (Markowitz; Sharpe), which propagate within days through correlated asset repricing and intermediary balance-sheet adjustments (Adrian and Shin; Brunnermeier and Pedersen). The real-economy response has to work through investment timing (Jermann and Quadrini; Bloom), wealth-effect consumption (Case, Quigley, and Shiller), and credit-channel effects on firm balance sheets (Bernanke, Gertler, and Gilchrist) — each of which has inherent lags.

Why inflation is the standout. The 18% long-horizon price-level variance contribution is quantitatively much larger than the real-activity contributions, which is consistent with demand-side transmission: the financial-market response raises aggregate demand via wealth and risk-appetite channels, but supply-side adjustment takes time, so prices move first and further than quantities. This pattern distinguishes crypto shocks from pure financial disturbances (which typically have smaller and less persistent price effects) and suggests they behave more like demand shocks with a financial-market entry point.

Related questions: Do crypto shocks cause inflation? · Why use Pandemic Priors?

How do you estimate a crypto-to-macro VAR cleanly through the COVID-19 period?

Use Pandemic Priors. Standard Bayesian VAR priors (Minnesota) treat 2020 observations like any other, which distorts estimated persistence and impulse-response dynamics because a handful of extreme pandemic data points dominate the likelihood. Cascaldi-Garcia (2022) proposes adding 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 DM4 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.

Implementation recipe. 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 from Bayesian draws.

Robustness. Main findings are stable under:

Alternative 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.
Narrative validation via Romer-Romer-style event regression on six categories of crypto-market events, following Romer and Romer (2004).

Data and reproducibility

Cryptocurrency prices: CoinMarketCap (daily, aggregated to monthly).
Macroeconomic data: 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.
Sample: January 2015 – November 2024, monthly frequency.
Software: Bayesian SVAR estimation with Pandemic Priors (Cascaldi-Garcia 2022), φ = 0.1, λ = 0.2, 30-month impulse horizons, 68% posterior bands.
Replication code: available at robinchen.org upon publication.

Cite as: Chen, Z. (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},
 doi = {10.3390/jrfm18070360},
 url = {https://doi.org/10.3390/jrfm18070360},
 publisher = {MDPI},
 license = {CC BY 4.0}
}

Using advanced econometric modeling called Bayesian Structural Vector Autoregression (BSVAR) — a statistical method that can identify cause-and-effect relationships between economic variables — Chen analyzed data from 2015 to 2024 to trace how cryptocurrency price shocks transmit through the economy.

The results are striking. Cryptocurrency shocks now explain in 30-month horizon:

18% of stock market price fluctuations
27% of commodity price movements
18% of long-term inflation variance

These findings suggest that cryptocurrency has achieved what economists call “systemic importance” — meaning its movements can meaningfully affect the entire economic system.

How Cryptocurrency Affects the Economy

The study identifies two primary transmission channels:

1. Financial Market Integration

When cryptocurrency prices rise, they create spillover effects to other financial assets through portfolio rebalancing — the process by which investors adjust their holdings to maintain optimal risk-return profiles. This validates modern portfolio theory from Markowitz (1952), which explains how price movements in one asset class can propagate to others through investor behavior.

2. Real Economic Effects

Cryptocurrency price movements also affect the “real economy” — actual production, employment, and consumption — though these effects are more modest. The study found that positive cryptocurrency shocks lead to:

0.15% increase in industrial production (with a delay, reflecting the time needed for investment decisions)
0.02% decrease in unemployment
Persistent inflationary pressure of 0.15%

These effects operate through what economists call the wealth effect — when asset price increases make people feel richer and spend more — and investment channels, where changing asset prices influence business investment decisions.

What Drives Cryptocurrency Markets?

Using narrative analysis — a method that matches statistical results with historical events — Chen found that cryptocurrency shocks are primarily driven by:

Sentiment events (strongest effect): Market psychology, institutional adoption announcements, and public perception changes
Technology developments: Protocol upgrades, network improvements, and technical innovations

Interestingly, regulatory and monetary policy changes showed statistically insignificant effects, contradicting some earlier studies that emphasized policy uncertainty as a primary driver (Auer & Claessens, 2018; Chokor & Alfieri, 2021).

Methodological Innovation: Handling the COVID-19 Challenge

A key methodological contribution is the use of Pandemic Priors — a statistical technique developed by Cascaldi-Garcia (2022) to handle the extreme economic disruptions during COVID-19. Traditional econometric models can be thrown off by such unusual periods, but this approach allows researchers to extract meaningful insights while accounting for the pandemic’s extraordinary effects.

Policy Implications

The findings carry important implications for policymakers:

For Central Banks: Since cryptocurrency shocks contribute 18% to long-term inflation variance, monetary authorities should monitor these markets for demand-driven inflation pressures and incorporate cryptocurrency developments into their economic forecasting.

For Financial Regulators: Cryptocurrency markets should be monitored as sources of systematic risk, given their substantial contribution to financial market volatility. However, regulators should distinguish between sentiment-driven volatility and technology-driven value creation.

A Fundamental Shift in the Financial Architecture

This research documents a fundamental shift in how we should think about cryptocurrency. As Chen notes, “Rather than simply providing portfolio diversification, cryptocurrencies now function as systematic risk amplifiers.” The study suggests that “the era of treating cryptocurrency markets as a separate, disconnected asset class may be coming to an end.”

The findings align with recent work by Charfeddine et al. (2020) showing increased correlations between cryptocurrencies and traditional assets during market stress, and contradict earlier studies like Bouri et al. (2017) that emphasized cryptocurrencies’ diversification benefits.

Looking Forward

While this study focuses on Bitcoin and covers a relatively short time period, it establishes a framework for understanding cryptocurrency’s macroeconomic transmission mechanisms. Future research could examine:

Different cryptocurrencies’ varying effects
Longer-term structural relationships
Nonlinear effects and regime changes

The integration of cryptocurrency markets with traditional financial and economic systems represents what Chen calls “a fundamental shift in the global financial architecture.” As institutional adoption continues and market capitalization grows, understanding these transmission mechanisms becomes increasingly crucial for both policymakers and investors.

Chen, Zhengyang. 2025. “From Disruption to Integration: Cryptocurrency Prices, Financial Fluctuations, and Macroeconomy” Journal of Risk and Financial Management 18, no. 7: 360. https://doi.org/10.3390/jrfm18070360

📚 Academic Citations & Literature Review

Papers and Topics That Could Cite This Research

This study on cryptocurrency’s macroeconomic transmission effects could be cited across multiple academic disciplines and research areas. Here’s a detailed breakdown of potential citing opportunities:

Monetary Economics and Central Banking

Monetary Policy Research

Inflation targeting in the digital age: Papers examining how central banks should modify inflation targeting frameworks to account for cryptocurrency-driven price pressures
Monetary transmission mechanism studies: Research updating traditional transmission channels to include digital asset pathways
Central Bank Digital Currency (CBDC) research: Studies comparing CBDC effects with private cryptocurrency impacts on monetary policy effectiveness
Unconventional monetary policy: Papers on quantitative easing effects when alternative assets like crypto provide portfolio substitution

Central Banking Practice

Financial stability monitoring: Research on incorporating cryptocurrency metrics into financial stability frameworks
Macroprudential policy: Studies on whether crypto markets require specific regulatory tools
Monetary policy communication: Papers on how central banks should communicate about cryptocurrency risks and opportunities

Financial Economics

Asset Pricing and Portfolio Theory

Modern portfolio theory extensions: Research updating Markowitz (1952) frameworks to include cryptocurrency correlation structures
Risk factor models: Studies incorporating cryptocurrency as a systematic risk factor (building on Chen’s 18% equity variance contribution finding)
Alternative investment strategies: Papers on optimal cryptocurrency allocation in institutional portfolios
Volatility spillover studies: Research on contagion mechanisms between crypto and traditional assets

Financial Integration and Contagion

Systemic risk assessment: Studies using Chen’s methodology to quantify crypto’s systemic importance in different markets
Crisis transmission: Research on how cryptocurrency markets amplify or dampen financial crises
Cross-border financial spillovers: Papers on how cryptocurrency transmission varies across countries
Market microstructure: Studies on high-frequency transmission mechanisms between crypto and traditional markets

Macroeconomics

Business Cycle Research

Real business cycle models: Papers incorporating cryptocurrency wealth effects into DSGE (Dynamic Stochastic General Equilibrium) models
Investment and growth: Studies on how cryptocurrency price volatility affects business investment decisions through the channels Chen identifies
Consumption smoothing: Research on how cryptocurrency wealth affects household consumption patterns
Economic forecasting: Papers improving macroeconomic forecasts by including cryptocurrency variables

International Economics

Exchange rate determination: Studies on how cryptocurrency markets affect traditional currency relationships
Capital flows: Research on cryptocurrency’s role in international capital mobility
Global economic integration: Papers on whether cryptocurrency creates new forms of economic interdependence
Emerging market dynamics: Studies on cryptocurrency adoption in developing economies and macroeconomic effects

Financial Regulation and Policy

Regulatory Economics

Optimal cryptocurrency regulation: Papers using Chen’s findings to design regulatory frameworks that balance innovation with stability
Cross-border regulatory coordination: Studies on international policy coordination for systemically important crypto markets
Market surveillance: Research on monitoring tools for cryptocurrency’s macroeconomic effects
Prudential regulation: Papers on bank exposure limits to cryptocurrency-related assets

Financial Stability

Stress testing methodologies: Studies incorporating cryptocurrency shocks into bank and system stress tests
Early warning systems: Research on cryptocurrency indicators for predicting financial instability
Resolution frameworks: Papers on how to handle failures of systemically important crypto institutions
Deposit insurance: Studies on whether crypto-exposed institutions require different insurance frameworks

Behavioral and Experimental Economics

Market Psychology

Sentiment transmission mechanisms: Papers expanding on Chen’s sentiment findings using experimental or survey methods
Investor behavior: Studies on how retail vs. institutional investors differently transmit crypto shocks
Herding and momentum: Research on behavioral factors amplifying cryptocurrency’s macroeconomic effects
Risk perception: Papers on how cryptocurrency volatility affects broader risk appetite

Technology Adoption

Innovation diffusion: Studies on how technological developments in crypto markets affect economic adoption patterns
Network effects: Research expanding on Chen’s network effect findings in cryptocurrency ecosystems
Digital transformation: Papers on cryptocurrency’s role in broader economic digitization

Development Economics

Financial Inclusion

Cryptocurrency and financial access: Studies on how crypto markets affect financial inclusion and development outcomes
Remittances and payments: Research on cryptocurrency’s macroeconomic effects in remittance-dependent economies
Monetary sovereignty: Papers on how cryptocurrency adoption affects developing country monetary policy
Dollarization studies: Research comparing cryptocurrency adoption to traditional currency substitution

Economic Development

Infrastructure and growth: Studies on how cryptocurrency infrastructure affects economic development
Institutional development: Research on cryptocurrency’s interaction with traditional financial institutions in developing markets
Capital formation: Papers on how cryptocurrency markets affect savings and investment in emerging economies

Econometric Methodology

Time Series Analysis

VAR methodology improvements: Papers refining structural identification techniques for cryptocurrency analysis
Pandemic econometrics: Studies applying or improving Chen’s Pandemic Priors methodology to other research questions
Narrative identification: Research extending narrative approaches to other asset classes or economic shocks
Forecast evaluation: Papers comparing traditional vs. crypto-augmented forecasting models

Causal Inference

Shock identification: Studies using alternative identification strategies to validate Chen’s transmission mechanism findings
Natural experiments: Research using regulatory changes or technological events as instruments for cryptocurrency effects
Machine learning approaches: Papers using AI methods to identify cryptocurrency transmission patterns

Technology and Innovation Studies

Financial Technology

Blockchain economics: Studies on how blockchain adoption affects traditional economic relationships
Digital asset ecosystem: Research on interactions between different cryptocurrency platforms and economic effects
Decentralized finance (DeFi): Papers on how DeFi protocols interact with traditional financial transmission mechanisms
Stablecoin research: Studies on how different cryptocurrency types have varying macroeconomic effects

Innovation Policy

Technology regulation: Research on optimal policies for emerging financial technologies
Innovation spillovers: Studies on how cryptocurrency innovation affects other sectors
Digital infrastructure: Papers on the macroeconomic effects of financial technology infrastructure

International Finance

Global Financial Markets

Safe haven assets: Studies comparing cryptocurrency to traditional safe havens during crises
Reserve asset diversification: Research on cryptocurrency’s potential role in central bank reserves
Global liquidity: Papers on how cryptocurrency markets affect international liquidity transmission
Financial globalization: Studies on cryptocurrency’s role in financial market integration

Exchange Rate Economics

Currency substitution: Research on cryptocurrency adoption’s effects on traditional currencies
Purchasing power parity: Studies incorporating cryptocurrency markets into exchange rate models
Capital controls: Papers on how cryptocurrency markets circumvent or interact with capital restrictions

Environmental and Energy Economics

Sustainability Studies

Energy consumption: Papers citing Chen’s findings while examining environmental costs of cryptocurrency’s economic integration
Sustainable finance: Research on how cryptocurrency’s macroeconomic role affects ESG investing
Green monetary policy: Studies on incorporating environmental considerations into crypto-aware monetary frameworks

Economic History and Comparative Studies

Historical Perspectives

Monetary history: Papers comparing cryptocurrency adoption to historical monetary innovations
Financial innovation: Studies comparing cryptocurrency’s economic integration to past financial innovations
Crisis comparisons: Research comparing cryptocurrency’s role in recent vs. historical financial crises

Cross-Country Studies

Comparative monetary systems: Research comparing cryptocurrency effects across different monetary regimes
Institutional differences: Studies on how institutional quality affects cryptocurrency’s macroeconomic transmission
Policy regime comparisons: Papers comparing cryptocurrency effects under different regulatory approaches

Specialized Applications

Insurance and Risk Management

Catastrophe modeling: Studies incorporating cryptocurrency volatility into disaster risk models
Pension fund management: Research on cryptocurrency exposure in long-term institutional portfolios
Sovereign risk: Papers on how cryptocurrency markets affect country risk assessments

Real Estate and Housing

Housing market dynamics: Studies on cryptocurrency wealth effects on real estate markets
Urban economics: Research on how cryptocurrency industries affect local economic development
Property investment: Papers on cryptocurrency’s interaction with real estate as alternative investments

Labor Economics

Employment effects: Studies expanding on Chen’s unemployment findings with sectoral or demographic detail
Wage dynamics: Research on how cryptocurrency markets affect wage setting and labor bargaining
Gig economy: Papers on cryptocurrency’s role in alternative work arrangements

Papers Relevant to Chen (2025)

Foundational Portfolio and Asset Pricing Theory

Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.

Chen’s findings on cryptocurrency spillovers to equity markets (18% variance contribution) directly validate and extend Markowitz’s modern portfolio theory by demonstrating how portfolio rebalancing mechanisms operate in practice with emerging asset classes. The paper provides empirical evidence for the theoretical prediction that assets with similar systematic risk exposures exhibit stronger comovement patterns.

Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. Journal of Finance, 19(3), 425-442.

Chen’s research confirms CAPM predictions by showing that cryptocurrency shocks primarily affect equity markets while notably excluding bonds, reflecting the risk characteristics of cryptocurrencies as speculative growth assets rather than safe havens as predicted by systematic risk theory.

Tobin, J. (1958). Liquidity Preference as Behavior Towards Risk. Review of Economic Studies, 25(2), 65-86.

The delayed industrial production response (0.15%) found in Chen’s study strongly supports Tobin’s Q theory, where cryptocurrency price movements influence investment through relative capital costs, with timing reflecting real options effects where firms optimize irreversible investment decisions.

Behavioral Finance and Market Sentiment

Baker, M., & Wurgler, J. (2006). Investor Sentiment and the Cross‐Section of Stock Returns. Journal of Finance, 61(4), 1645-1680.

Chen’s narrative analysis revealing sentiment as the strongest driver of cryptocurrency shocks (coefficient = 1.36) directly validates Baker and Wurgler’s investor sentiment framework, demonstrating how mood-driven trading creates systematic factors affecting multiple asset classes beyond individual stocks.

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), 703-738.

Chen’s findings on sentiment-driven cryptocurrency transmission mechanisms provide modern empirical validation of the noise trader model, showing how sentiment shocks propagate across asset classes and affect real economic variables like unemployment and industrial production.

Financial Contagion and Spillover Effects

Forbes, K. J., & Rigobon, R. (2002). No Contagion, Only Interdependence: Measuring Stock Market Comovements. Journal of Finance, 57(5), 2223-2261.

Chen’s methodology using structural VAR with pandemic priors builds on Forbes and Rigobon’s contagion framework, but finds evidence of true spillover effects rather than just correlation, with cryptocurrency shocks explaining substantial variance in traditional financial markets even after controlling for common factors.

Diebold, F. X., & Yilmaz, K. (2012). Better to Give than to Receive: Predictive Directional Measurement of Volatility Spillovers. International Journal of Forecasting, 28(1), 57-66.

Chen’s variance decomposition results (18% equity, 27% commodities) complement Diebold-Yilmaz spillover methodology by providing structural identification of cryptocurrency as a source rather than recipient of financial market volatility, establishing directional causality through narrative validation.

Monetary Economics and Central Banking

Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.

Chen’s findings on monetary policy responses to cryptocurrency shocks (initial M4 expansion followed by contraction) extend Bernanke-Blinder’s monetary transmission framework to include how central banks respond to alternative asset price movements that generate inflationary pressures.

Christiano, L. J., Eichenbaum, M., & Evans, C. L. (1999). Monetary Policy Shocks: What Have We Learned and to What End? Handbook of Macroeconomics, 1, 65-148.

Chen adopts the Christiano-Eichenbaum-Evans recursive identification strategy while extending it to cryptocurrency markets, demonstrating how their established VAR methodology can be applied to understand transmission mechanisms of emerging financial innovations to macroeconomic variables.

Romer, C. D., & Romer, D. H. (2004). A New Measure of Monetary Shocks: Derivation and Implications. American Economic Review, 94(4), 1055-1084.

Chen’s narrative identification approach directly builds on Romer and Romer’s pioneering work by applying their narrative regression methodology to cryptocurrency markets, using historical events to validate structural shock identification and provide economic interpretation of estimated shock series.

Financial Accelerator and Credit Channels

Bernanke, B., Gertler, M., & Gilchrist, S. (1999). The Financial Accelerator in a Quantitative Business Cycle Framework. Handbook of Macroeconomics, 1, 1341-1393.

Chen’s finding that financial stress indicators improve following positive cryptocurrency shocks provides empirical support for financial accelerator mechanisms, where asset price changes affect balance sheets and credit conditions, subsequently influencing real economic activity through enhanced credit availability.

Kiyotaki, N., & Moore, J. (1997). Credit Cycles. Journal of Political Economy, 105(2), 211-248.

The simultaneous improvement in financial stress and real economic variables in Chen’s results aligns with Kiyotaki-Moore credit cycle theory, suggesting cryptocurrency appreciation strengthens balance sheets and relaxes borrowing constraints, amplifying the initial wealth effect through credit mechanisms.

Wealth Effects and Consumption

Case, K. E., Quigley, J. M., & Shiller, R. J. (2005). Comparing Wealth Effects: The Stock Market versus the Housing Market. Advances in Macroeconomics, 5(1), 1-32.

Chen’s findings on unemployment reduction (0.02%) following cryptocurrency shocks provide new evidence for wealth effect channels similar to Case-Quigley-Shiller’s housing wealth effects, though the magnitude suggests cryptocurrency wealth effects may be smaller than traditional asset wealth effects.

Ludvigson, S. C., Steindel, C., & Lettau, M. (2002). Monetary Policy Transmission Through the Consumption-Wealth Channel. FRBNY Economic Policy Review, 8(1), 117-133.

Chen’s identification of persistent inflationary pressure (0.15% PCE increase) following cryptocurrency shocks extends Ludvigson et al.’s consumption-wealth channel analysis to digital assets, showing how alternative asset appreciation can generate demand-driven inflation through household spending.

Cryptocurrency-Specific Literature

Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Bitcoin.org.

Chen’s research provides the first comprehensive macroeconomic analysis of Nakamoto’s innovation, demonstrating that Bitcoin has evolved from its original conception as electronic cash to become a systemically important financial asset with measurable effects on inflation, employment, and financial stability.

Yermack, D. (2015). Is Bitcoin a Real Currency? An Economic Appraisal. Handbook of Digital Currency, 31-43.

Chen’s findings directly challenge Yermack’s skeptical assessment of Bitcoin’s economic significance by providing empirical evidence that cryptocurrency markets now exhibit transmission mechanisms characteristic of systemically important assets, contradicting the view of Bitcoin as economically marginal.

Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of Exchange or Speculative Assets? Journal of International Financial Markets, Institutions and Money, 54, 177-189.

Chen’s sentiment-driven shock findings support Baur et al.’s characterization of Bitcoin as primarily speculative, while extending their analysis to show how speculative dynamics create real macroeconomic effects through financial market integration and wealth channels.

Regulatory and Policy Framework

Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, Technology, and Governance. Journal of Economic Perspectives, 29(2), 213-238.

Chen’s findings on the limited role of regulatory shocks in driving cryptocurrency prices provide nuanced evidence for Böhme et al.’s analysis of Bitcoin governance, suggesting that technological and sentiment factors may be more important than regulatory developments in determining market outcomes.

Pieters, G., & Vivanco, S. (2017). Financial Regulations and Price Inconsistencies Across Bitcoin Markets. Information Economics and Policy, 39, 1-14.

Chen’s methodology for identifying regulatory shocks builds on Pieters and Vivanco’s work on regulatory arbitrage, but finds that regulatory events are less systematically important for cryptocurrency transmission than previously thought, suggesting markets may have developed resilience to regulatory uncertainty.

International Finance and Capital Flows

Obstfeld, M., & Taylor, A. M. (2004). Global Capital Markets: Integration, Crisis, and Growth. Cambridge University Press.

Chen’s findings on cryptocurrency spillovers across asset classes contribute to Obstfeld-Taylor’s analysis of global financial integration by documenting how digital assets create new channels for international financial transmission that operate independently of traditional banking and capital flow mechanisms.

Rey, H. (2013). Dilemma not Trilemma: The Global Financial Cycle and Monetary Policy Independence. Proceedings - Economic Policy Symposium - Jackson Hole, 285-333.

Chen’s evidence of cryptocurrency’s role in financial market integration provides a new dimension to Rey’s global financial cycle framework, suggesting that decentralized digital assets may create additional challenges for monetary policy independence beyond traditional capital flow channels.

Asset Pricing Anomalies and Market Efficiency

Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.

Chen’s finding that sentiment shocks are the strongest driver of cryptocurrency markets relates to Jegadeesh-Titman’s momentum effects, suggesting that sentiment-driven price movements in crypto markets may create systematic momentum patterns that transmit to traditional asset classes.

Fama, E. F., & French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics, 33(1), 3-56.

Chen’s identification of cryptocurrency as a systematic risk factor (explaining 18% of equity variance) suggests the need to extend the Fama-French factor model to include digital asset factors, as cryptocurrency appears to represent a new common risk factor affecting traditional asset returns.

Crisis and Financial Stability

Brunnermeier, M. K. (2009). Deciphering the Liquidity and Credit Crunch 2007-2008. Journal of Economic Perspectives, 23(1), 77-100.

Chen’s use of pandemic priors to handle COVID-19 disruptions builds on Brunnermeier’s crisis analysis methodology, while the finding that cryptocurrency markets maintained transmission mechanisms during the pandemic suggests they may be more resilient to liquidity crises than traditional markets.

Adrian, T., & Shin, H. S. (2010). Liquidity and Leverage. Journal of Financial Intermediation, 19(3), 418-437.

Chen’s evidence of financial stress reduction following positive cryptocurrency shocks provides a counterpoint to Adrian-Shin’s procyclical leverage framework, suggesting that cryptocurrency appreciation may actually improve rather than worsen financial intermediary balance sheets and leverage capacity.

Econometric Methodology

Sims, C. A. (1980). Macroeconomics and Reality. Econometrica, 48(1), 1-48.

Chen’s structural VAR approach builds directly on Sims’ foundational methodology while extending it to incorporate cryptocurrency variables and pandemic-specific econometric adjustments, demonstrating the continued relevance of VAR methods for analyzing emerging economic phenomena.

Stock, J. H., & Watson, M. W. (2001). Vector Autoregressions. Journal of Economic Perspectives, 15(4), 101-115.

Chen’s implementation of Bayesian VAR with pandemic priors represents a methodological advancement over Stock-Watson’s framework, showing how traditional VAR techniques can be adapted to handle extreme observations while preserving structural relationships during crisis periods.

Uhlig, H. (2005). What Are the Effects of Monetary Policy on Output? Results from an Agnostic Identification Procedure. Journal of Monetary Economics, 52(2), 381-419.

Chen’s recursive identification strategy could be complemented by Uhlig’s sign restriction approach, as the clear theoretical predictions about cryptocurrency transmission mechanisms provide natural candidates for sign restrictions that could validate or extend Chen’s identification results.

Innovation and Technology Economics

Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. Harper & Brothers.

Chen’s finding that technology shocks are significant drivers of cryptocurrency markets provides modern empirical validation of Schumpeterian innovation theory, demonstrating how technological developments create economic value that transmits through financial markets to affect real economic activity.

Arrow, K. J. (1962). The Economic Implications of Learning by Doing. Review of Economic Studies, 29(3), 155-173.

Chen’s identification of network effects in cryptocurrency markets relates to Arrow’s learning-by-doing framework, as the growth of cryptocurrency networks creates positive feedback loops that enhance utility and economic value, leading to broader macroeconomic transmission effects.

Development Economics and Financial Inclusion

Demirgüç-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. World Bank Policy Research Working Paper.

Chen’s findings on cryptocurrency’s macroeconomic transmission mechanisms provide crucial evidence for understanding how digital financial innovations like those measured in the Global Findex can have economy-wide effects, suggesting that financial inclusion through cryptocurrency adoption may generate broader economic impacts than previously recognized.

Beck, T., Demirgüç-Kunt, A., & Levine, R. (2007). Finance, Inequality and the Poor. Journal of Economic Growth, 12(1), 27-49.

Chen’s identification of wealth effects from cryptocurrency appreciation offers a new perspective on Beck et al.’s finance-inequality relationship, as decentralized digital assets may provide alternative wealth accumulation channels that bypass traditional financial institutions, potentially affecting inequality through different transmission mechanisms.

Banerjee, A. V., & Duflo, E. (2014). Do Firms Want to Borrow More? Testing Credit Constraints Using a Directed Lending Program. Review of Economic Studies, 81(2), 572-607.

Chen’s evidence of improved financial stress indicators following cryptocurrency shocks suggests that digital asset appreciation may relax credit constraints similar to Banerjee-Duflo’s directed lending programs, providing an alternative mechanism for enhancing credit access in developing economies.

Morduch, J. (1999). The Microfinance Promise. Journal of Economic Literature, 37(4), 1569-1614.

Chen’s documentation of cryptocurrency’s real economic effects (industrial production, unemployment) provides macroeconomic validation for Morduch’s microfinance framework, suggesting that decentralized financial innovations may achieve similar development outcomes through different channels than traditional microfinance institutions.

Environmental and Energy Economics

de Vries, A. (2018). Bitcoin’s Growing Energy Problem. Joule, 2(5), 801-805.

Chen’s findings on cryptocurrency’s significant macroeconomic effects (18% of inflation variance) must be weighed against de Vries’ energy consumption analysis, creating important policy trade-offs between the documented economic benefits of cryptocurrency integration and environmental costs of energy-intensive mining operations.

Krugman, P. (2013). Bitcoin is Evil. New York Times.

Chen’s empirical evidence of cryptocurrency’s systematic economic importance directly challenges Krugman’s dismissive assessment, providing quantitative evidence that Bitcoin has achieved the scale and integration necessary to affect real economic variables, contradicting the view that it represents a wasteful energy sink.

Mora, C., Rollins, R. L., Taladay, K., et al. (2018). Bitcoin Emissions Alone Could Push Global Warming Above 2°C. Nature Climate Change, 8(11), 931-933.

Chen’s documentation of cryptocurrency’s macroeconomic transmission mechanisms adds complexity to Mora et al.’s climate analysis by demonstrating that cryptocurrency provides measurable economic benefits that must be considered alongside environmental costs in policy cost-benefit calculations.

Stoll, C., Klaaßen, L., & Gallersdörfer, U. (2019). The Carbon Footprint of Bitcoin. Joule, 3(7), 1647-1661.

Chen’s findings on cryptocurrency’s role in economic forecasting and monetary policy suggest that Stoll et al.’s carbon footprint analysis should incorporate the economic value of cryptocurrency’s contribution to financial stability and macroeconomic management when assessing net social costs.

Digital Economics and Platform Theory

Parker, G. G., Van Alstyne, M. W., & Choudary, S. P. (2016). Platform Revolution: How Networked Markets Are Transforming the Economy and How to Make Them Work for You. W. W. Norton & Company.

Chen’s identification of network effects as drivers of cryptocurrency shocks validates Parker et al.’s platform theory in the context of decentralized financial networks, demonstrating how network externalities in cryptocurrency ecosystems create macroeconomic transmission effects similar to traditional platform businesses.

Rochet, J. C., & Tirole, J. (2003). Platform Competition in Two-Sided Markets. Journal of the European Economic Association, 1(4), 990-1029.

Chen’s evidence of cryptocurrency’s financial market integration relates to Rochet-Tirole’s two-sided market framework, as cryptocurrency platforms facilitate interactions between different types of users (investors, developers, merchants) while creating economy-wide spillover effects through network growth.

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

Chen’s findings on technology shocks as significant drivers of cryptocurrency markets provide empirical support for Brynjolfsson-McAfee’s digital transformation thesis, showing how technological innovations in blockchain systems create measurable macroeconomic effects through financial market channels.

Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs.

Chen’s analysis of cryptocurrency’s decentralized transmission mechanisms offers an alternative to Zuboff’s surveillance capitalism framework, suggesting that decentralized digital assets may provide economic value creation without the centralized data extraction that characterizes traditional digital platforms.

Labor Economics and Future of Work

Autor, D. H., Levy, F., & Murnane, R. J. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of Economics, 118(4), 1279-1333.

Chen’s evidence of unemployment reduction following cryptocurrency shocks provides a new perspective on Autor et al.’s skill-biased technological change hypothesis, suggesting that blockchain technologies may create employment effects through financial wealth channels rather than direct skill substitution.

Acemoglu, D., & Restrepo, P. (2018). The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review, 108(6), 1488-1542.

Chen’s findings on cryptocurrency’s real economic transmission complement Acemoglu-Restrepo’s automation analysis by showing how financial innovations create employment effects through aggregate demand channels, potentially offsetting job displacement from other technological changes.

Katz, L. F., & Krueger, A. B. (2019). The Rise and Nature of Alternative Work Arrangements in the United States, 1995-2015. ILR Review, 72(2), 382-416.

Chen’s documentation of cryptocurrency’s macroeconomic effects provides important context for Katz-Krueger’s gig economy analysis, as digital asset appreciation may affect the financial stability and economic security of alternative work arrangements through wealth and portfolio effects.

Frey, C. B., & Osborne, M. A. (2017). The Future of Employment: How Susceptible Are Jobs to Computerisation? Technological Forecasting and Social Change, 114, 254-280.

Chen’s evidence that cryptocurrency shocks affect unemployment through wealth rather than technological displacement channels offers a different perspective on Frey-Osborne’s automation concerns, suggesting that blockchain innovations may create employment through financial rather than technological mechanisms.

Urban Economics and Regional Development

Glaeser, E. L., & Gottlieb, J. D. (2009). The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States. Journal of Economic Literature, 47(4), 983-1028.

Chen’s findings on cryptocurrency’s real economic effects could have important implications for Glaeser-Gottlieb’s urban agglomeration analysis, as cryptocurrency wealth concentration in specific geographic areas may affect local economic development and spatial equilibrium through the transmission mechanisms Chen identifies.

Moretti, E. (2012). The New Geography of Jobs. Houghton Mifflin Harcourt.

Chen’s evidence of cryptocurrency’s impact on industrial production and employment relates to Moretti’s analysis of innovation clusters, as blockchain industry development may create similar agglomeration effects and local economic spillovers through the macroeconomic channels Chen documents.

Autor, D., Dorn, D., & Hanson, G. (2013). The China Syndrome: Local Labor Market Effects of Import Competition in the United States. American Economic Review, 103(6), 2121-2168.

Chen’s methodology for identifying cryptocurrency transmission effects could be extended to analyze regional variation in cryptocurrency adoption, potentially providing insights into how digital asset markets affect local labor markets differently than the trade shocks analyzed by Autor-Dorn-Hanson.

Public Finance and Taxation

Mankiw, N. G., Weinzierl, M., & Yagan, D. (2009). Optimal Taxation in Theory and Practice. Journal of Economic Perspectives, 23(4), 147-174.

Chen’s findings on cryptocurrency’s macroeconomic importance raise questions about optimal taxation of digital assets, as the 18% contribution to inflation variance suggests that cryptocurrency taxation policies could have significant macroeconomic effects beyond traditional revenue considerations.

Saez, E., & Zucman, G. (2019). The Triumph of Injustice: How the Rich Dodge Taxes and How to Make Them Pay. W. W. Norton & Company.

Chen’s evidence of cryptocurrency wealth effects provides important context for Saez-Zucman’s wealth inequality analysis, as digital asset appreciation may represent a new form of wealth accumulation that requires consideration in progressive taxation frameworks.

Piketty, T. (2014). Capital in the Twenty-First Century. Harvard University Press.

Chen’s documentation of cryptocurrency as a new asset class with significant economic transmission effects adds complexity to Piketty’s r > g framework, as digital assets may represent a new form of capital that exhibits different accumulation dynamics than traditional assets.

Corporate Finance and Investment

Modigliani, F., & Miller, M. H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. American Economic Review, 48(3), 261-297.

Chen’s findings on cryptocurrency’s effect on industrial production through investment channels relate to Modigliani-Miller’s capital structure theory, as cryptocurrency price movements may affect firms’ cost of capital and investment decisions through balance sheet effects and alternative financing mechanisms.

Myers, S. C., & Majluf, N. S. (1984). Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have. Journal of Financial Economics, 13(2), 187-221.

Chen’s evidence of cryptocurrency’s transmission to real economic activity through financial stress channels relates to Myers-Majluf’s pecking order theory, as cryptocurrency appreciation may provide alternative financing sources that affect firms’ capital structure decisions and investment timing.

Jensen, M. C. (1986). Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers. American Economic Review, 76(2), 323-329.

Chen’s documentation of cryptocurrency’s wealth effects on corporate investment relates to Jensen’s free cash flow hypothesis, as cryptocurrency appreciation may provide firms with additional financial resources that affect investment efficiency and agency relationships.

Insurance and Risk Management

Arrow, K. J. (1963). Uncertainty and the Welfare Economics of Medical Care. American Economic Review, 53(5), 941-973.

Chen’s findings on cryptocurrency’s role in financial stress reduction relate to Arrow’s analysis of uncertainty and insurance, as digital assets may provide portfolio diversification benefits that reduce systemic risk, though the transmission mechanisms Chen identifies suggest they may also create new sources of systematic risk.

Rothschild, M., & Stiglitz, J. (1976). Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information. Quarterly Journal of Economics, 90(4), 629-649.

Chen’s evidence of cryptocurrency’s correlation with traditional financial markets challenges Rothschild-Stiglitz’s risk pooling framework, as the spillover effects Chen documents suggest that cryptocurrency may not provide the independent risk diversification that traditional insurance theory assumes.

Grossman, M. (1972). On the Concept of Health Capital and the Demand for Health. Journal of Political Economy, 80(2), 223-255.

Chen’s methodology for analyzing cryptocurrency transmission effects could be extended to examine health impacts of financial stress reduction, as the improvement in financial conditions following cryptocurrency appreciation may affect health outcomes through the mechanisms Grossman identifies.

Case, A., & Deaton, A. (2015). Rising Morbidity and Mortality in Midlife Among White Non-Hispanic Americans in the 21st Century. Proceedings of the National Academy of Sciences, 112(49), 15078-15083.

Chen’s evidence of unemployment reduction following cryptocurrency shocks may relate to Case-Deaton’s analysis of mortality trends, as improved labor market conditions through cryptocurrency wealth effects could potentially affect health outcomes in affected populations.

Political Economy and Institutions

Acemoglu, D., & Robinson, J. A. (2012). Why Nations Fail: The Origins of Power, Prosperity, and Poverty. Crown Business.

Chen’s findings on cryptocurrency’s macroeconomic transmission raise important questions about Acemoglu-Robinson’s institutional framework, as decentralized digital assets may provide alternative economic development paths that operate independently of traditional extractive or inclusive political institutions.

North, D. C. (1990). Institutions, Institutional Change and Economic Performance. Cambridge University Press.

Chen’s evidence of cryptocurrency’s economic integration relates to North’s institutional analysis, as blockchain technologies represent new institutional arrangements for conducting economic transactions that may affect macroeconomic performance through the transmission mechanisms Chen identifies.

Olson, M. (1965). The Logic of Collective Action: Public Goods and the Theory of Groups. Harvard University Press.

Chen’s analysis of network effects in cryptocurrency markets relates to Olson’s collective action framework, as decentralized networks must overcome coordination problems to achieve the scale necessary for macroeconomic transmission effects, suggesting new solutions to collective action challenges.

Economic History and Long-term Development

Kindleberger, C. P. (1978). Manias, Panics, and Crashes: A History of Financial Crises. Basic Books.

Chen’s evidence of cryptocurrency’s sentiment-driven nature and financial market integration provides a modern case study for Kindleberger’s analysis of financial manias, though the persistent real economic effects Chen documents suggest cryptocurrency markets may exhibit different dynamics than historical bubbles.

Rajan, R. G., & Zingales, L. (2003). The Great Reversals: The Politics of Financial Development. Journal of Financial Economics, 69(1), 5-50.

Chen’s documentation of cryptocurrency’s role in financial development relates to Rajan-Zingales’ analysis of financial system evolution, as decentralized digital assets may represent a new form of financial development that operates independently of traditional political economy constraints.

Ferguson, N. (2008). The Ascent of Money: A Financial History of the World. Penguin Press.

Chen’s findings on cryptocurrency’s macroeconomic transmission provide a contemporary chapter for Ferguson’s financial history framework, demonstrating how new forms of money and financial innovation continue to shape economic development and macroeconomic relationships.

Game Theory and Mechanism Design

Myerson, R. B. (1991). Game Theory: Analysis of Conflict. Harvard University Press.

Chen’s analysis of cryptocurrency network effects relates to Myerson’s game theory framework, as the coordination required for cryptocurrency adoption and the resulting macroeconomic effects represent solutions to complex coordination games with multiple equilibria.

Hurwicz, L. (1973). The Design of Mechanisms for Resource Allocation. American Economic Review, 63(2), 1-30.

Chen’s evidence of cryptocurrency’s economic transmission relates to Hurwicz’s mechanism design theory, as blockchain protocols represent decentralized mechanisms for resource allocation that achieve macroeconomic effects without traditional centralized coordination.

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.

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}
}

Bayesian SVAR | Robin Chen

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Data and reproducibility

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Looking Forward

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