Zhengyang Chen | Macro Paper Warehouse

A granular investigation on the stability of money demand

This 2024 Macroeconomic Dynamics paper by Zhengyang Chen and Victor J. Valcarcel asks whether the long-documented instability of U.S. money demand reflects a genuine structural break in households’ and firms’ preferences for monetary assets, or is instead an artifact of measuring money with simple-sum aggregates (which just add up dollar balances) rather than Divisia aggregates (which weight each monetary asset by its real user cost, i.e., the foregone return from holding it instead of a benchmark asset). Using monthly U.S. data from January 1967 to March 2020 (the sample stops there because the Federal Reserve’s April 2020 redefinition of M1 and a simultaneous Center for Financial Stability accounting change make later observations non-comparable), the authors estimate bivariate Johansen (1991, 1995) cointegrating VECMs between real money balances scaled by nominal income and an opportunity-cost variable – either the 3-month T-bill yield or each aggregate’s own Divisia real user cost – in both semi-log (Cagan) and double-log (Meltzer) functional forms, checked across four Johansen trend specifications and corroborated with Andrews-Ploberger (1994) and Bai-Perron (2003) structural-break tests. They find that simple-sum M2 and M3 fail to cointegrate with the T-bill yield over the full sample, whereas Divisia M2 cointegrates robustly with its own user cost under every specification and functional form, and Divisia M3 cointegrates under most specifications; splitting the sample at the 1980 DIDMCA deregulation break (located via Andrews-Ploberger at 1979:M10 for Divisia M2 and 1980:M2 for Divisia M3) shows simple-sum M2’s relationship with the T-bill yield breaking down after 1980 (surviving only in trend specifications, and then with the wrong sign), while Divisia M2 continues to cointegrate correctly in both subperiods. After the Global Financial Crisis, the near-zero T-bill yield loses its cointegrating relationship with Divisia M3 and M4 entirely, yet both continue to cointegrate with their own real user costs, which never collapsed to zero. A granular decomposition of ten individual Divisia components against their own user costs finds that 29 of 40 estimated coefficients (10 components times 4 Johansen criteria) carry the theoretically correct sign, versus much weaker and more often wrong-signed cointegration between those same components and the T-bill yield. The authors conclude that the instability documented in the money-demand literature is a matter of measurement – the T-bill yield and simple-sum aggregation strip out information that Divisia aggregation and its user costs preserve – rather than a structural shift in money demand itself; scope is limited throughout to bivariate (two-variable) cointegrating relationships, with income elasticity imposed as unity rather than estimated.

Demystifying monetary policy surprises: Fed response to financial conditions and wait and see for new economic data

This 2026 Journal of Macroeconomics paper by Zhengyang Chen asks why high-frequency monetary policy surprises (MPS) – changes in short-term interest-rate futures measured in narrow windows around FOMC announcements, which are designed to be exogenous policy shocks – are nonetheless partially and systematically predictable from information available before the meeting. Chen’s proposed explanation is that the Fed responds primarily to broad financial conditions, using them as a summary statistic for the economic outlook, while adopting a “wait-and-see” posture toward newly released economic data in the weeks just before a meeting, and that markets fail to fully account for this reaction function when they set pre-meeting expectations. The paper combines three components: a Taylor-type theoretical model in which the policy rate responds to pre-announcement financial conditions rather than directly to economic data; high-frequency event-study regressions with HAC-robust standard errors, estimated over samples of up to 169 scheduled and unscheduled FOMC announcements between January 2000 and December 2019 (the COVID period is excluded, though the authors report the pattern is robust to including it in an unreported test); and a monthly proxy SVAR estimated in levels with 12 lags over January 1995-December 2023 (with the instrument-based identification itself restricted to January 2000-December 2019) that uses the Nakamura-Steinsson (2018) surprise as an external instrument following the Gertler-Karadi (2015) approach. Using the OFR Financial Stress Index (FSI) as the main financial-conditions proxy, the paper finds that the FSI level on the day before a meeting significantly and negatively predicts several raw (unorthogonalized) MPS measures – for example a coefficient of -0.25 (t = -3.18) for the combined MPS measure and -0.31 (t = -6.06) for the Nakamura-Steinsson measure – while the FSI’s own change is insignificant on the announcement day itself but strongly positive the day after (0.37, t = 4.75), which the authors interpret as evidence that FSI responds to the monetary surprise rather than a time-varying risk premium driving the correlation. After controlling for financial conditions, a more positive real-activity surprise (Scotti 2016) predicts a more dovish MPS (coefficient -0.19, t = -3.35 for MPS), the opposite of what a Fed reacting quickly to incoming news would imply, and this “wait-and-see” predictability pattern is detectable for real-activity data released up to roughly two weeks before a meeting but fades for older data. Purging the six Bauer-Swanson (2023b) documented MPS predictors of their financial-conditions component collapses their explanatory power for MPS (adjusted R-squared falling from about 12% to roughly 3% or less across several MPS measures), and instrumenting the proxy SVAR with the Nakamura-Steinsson surprise purged of its financial-conditions correlation removes the price- and output-puzzle impulse responses that appear when the raw surprise is used as the instrument.

Modeling inflation expectations in forward-looking interest rate and money growth rules

This 2025 Journal of Economic Dynamics and Control paper by Zhengyang Chen and Victor J. Valcarcel proposes a “rational expectations structural VAR” (RE-SVAR) — a way to embed rational expectations directly into a low-dimensional structural VAR without mapping the system to a fully specified DSGE model — and uses it to compare two candidate monetary-policy indicators, the Wu and Xia (2016) shadow federal funds rate and Divisia M4 money growth, on which produces a larger share of theoretically sensible (“non-puzzling”) impulse responses. The model starts from a three-equation consensus New Keynesian system (a forward-looking monetary policy rule written in either an interest-rate or a money-growth form, an IS equation, and a Phillips-curve AS equation, all estimated simultaneously) and identifies the monetary policy shock using a rational-expectations forecast-revision restriction that recovers it as a linear combination of reduced-form VAR residuals — not a Cholesky/recursive scheme, and without imposing any delayed-reaction exclusion restriction on the policy indicator. Rather than estimating the policy rule’s forward-looking parameters, the paper runs a “pseudo-calibration” grid search over the inflation- and output-response coefficients (φ_π, φ_y, each cycling over 61 values from 0 to 4) and the horizons over which expectations are formed (h_π = 0,…,12 months for inflation, h_y = 0,…,4 months for output), yielding 241,865 structural VAR specifications (8 lags, monthly U.S. data) and a resulting “cloud” of impulse responses; a response counts as a “puzzle” if output or inflation turns negative at any point within the first year following an expansionary shock to the policy indicator. In the main October 1988-February 2020 sample (377 monthly observations), the shadow federal funds rate produces output puzzles in 98.68% of the 241,865 specifications and inflation puzzles in 99.13%, with only 0.87% (2,109 specifications) surviving a no-joint-puzzle criterion; replacing it with Divisia M4 growth as the policy indicator cuts output puzzles to 4.02% and inflation puzzles to 4.13%, with 95.85% (231,825 specifications) surviving. This Divisia advantage holds up under a coarser 25,137-specification grid, a post-Global-Financial-Crisis effective-lower-bound sample (December 2008-February 2020, where even the shadow rate’s best-case output and inflation puzzle rates remain high at 72% and 93%), a long historical sample back to January 1967, a narrower Divisia M2 aggregate, and a PCE-based inflation measure — with one partial exception: in the long historical sample under PCE, the output-puzzle gap between indicators narrows substantially (53.3% for the shadow rate vs. 56.0% for DM4 vs. 47.9% for DM2). Longer inflation-expectation horizons help Divisia far more than the shadow rate (at a 12-month horizon, 18,430 of 241,865 DM4 specifications are non-puzzling versus only 5 for the shadow rate), and extending the model to four variables by adding the Gilchrist-Zakrajsek excess bond premium (July 1979-February 2020 sample) still yields an 81.45% no-joint-puzzle survival rate with DM4 as the indicator, alongside IS- and AS-shock responses that are broadly consistent with textbook signs. The authors read the results as evidence that the federal funds rate — especially its shadow-rate extension through the effective lower bound — is an inadequate standalone policy indicator in low-dimensional VARs, and that Divisia M4’s richer information content lets it capture forward-looking central bank behavior without added information variables, while cautioning that the RE-SVAR’s validity is conditional on the sensibility of its underlying three-equation theoretical structure and that, unlike a standard recursive VAR, it is not modular: adding any variable (as they do, only loosely, for the excess bond premium) requires specifying a full structural equation for it.

Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle

This 2021 Journal of Economic Dynamics and Control paper by Zhengyang Chen and Victor Valcarcel shows that the Wu-Xia (2016) shadow federal funds rate — a standard modern-sample proxy for the stance of monetary policy that extends below the zero lower bound — produces a persistent, statistically significant price puzzle in a time-varying-parameter VAR estimated on 1988-2020 U.S. data, and that this puzzle survives the standard fixes (adding commodity prices, federal funds futures, or forward rates) that resolved the price puzzle in earlier, pre-1988 samples. In its place, the authors propose Divisia monetary aggregates — weighted monetary aggregates that account for the different liquidity services of component assets, rather than simply summing dollar balances — as an alternative policy indicator: using Divisia M4 (and, with more muted magnitudes, the narrower Divisia M2) in place of the shadow rate produces no puzzling price response in the first three months and the theoretically correct price-level increase at 18-, 30-, and 60-month horizons following an expansionary shock, a correction the authors show is not an artifact of their time-varying estimation approach since it also holds in a constant-parameter VAR. Extending the analysis to the transmission of monetary shocks into 14 disaggregated money-market components (spanning currency, deposits, retail and institutional money-market funds, time deposits, repurchase agreements, commercial paper, and Treasury bills), the paper documents that transmission strengthened substantially after the 2007 financial crisis, with patterns in the responses of savings deposits and less-liquid institutional instruments that the authors interpret as evidence of a “flight-to-safety” effect among both households and firms during and after the crisis. The authors attribute the shadow rate’s breakdown as a policy indicator to the Federal Reserve’s increased forward-looking transparency (making it harder to generate a true interest-rate “surprise”) and to the post-crisis shift from reserve scarcity to reserve abundance, concluding that reintroducing monetary aggregates — measured correctly via the Divisia index rather than simple summation — may be “the missing piece of the puzzle” in a low-rate environment where a key short-term policy rate is highly persistent.