"Membership Turnover and Policy Disagreement at the FOMC" (August 2025), with A. Riboni.
This paper examines the implications of turnover for decision-making by the Federal Open Market Committee (FOMC). The analysis is based on a voting model where 1) the chair has agenda-setting powers but must secure the median's approval for her proposal to pass, and 2) the identity of the median changes stochastically over time through voting-right rotation and partisan appointments. The method-of-moments estimation of the model using data from 274 FOMC meetings delivers estimates of the chair and median preference parameters along a dovish-hawkish scale and of their disagreement in each meeting in the sample. Results indicate that disagreement increases the probability of dissents and policy inertia, but that turnover does not increase the volatility of interest rate adjustments.
"How Should a Central Bank Respond to Extreme Events?" (revised: May 2025), with J. Kim.
Extreme value theory is used to examine the effects of exceptionally large shocks on the U.S. economy and the appropriate monetary policy response. We construct and estimate a nonlinear multi-sector model where fluctuations are driven by shocks drawn from asymmetric extreme value distributions. The model is used to evaluate a leaning policy whereby the central bank responds directly to aggregate supply and demand shocks, in addition to inflation and employment. The U.S. data favor a specification where the aggregate shocks are negatively skewed so that extreme negative events occur with positive probability, and the central bank limits their effect by means of looser monetary policy. Compared with the optimal Ramsey policy, the leaning policy attaches a larger weight to output than to inflation stabilization. This result suggests that the large increases in the money supply in response to the COVID pandemic, with its subsequent inflation surge, may have been sub-optimal. This paper was previously circulated under the title "The Macroeconomic Effects of Extreme Shocks."
"Skewed Fluctuations and Propagation Through Production Networks" (April 2025), with K. Kamepalli and S. Ng. Revise and resubmit requested by Quantitative Economics.
Skewness is a prevalent feature of macroeconomic time series and may arise exogenously because shocks are asymmetrically distributed, or endogenously, as shocks propagate through production networks. Previous theoretical work often studies these two possibilities in isolation. We nest all possible sources of skewness in a model where output has a network, a common, and an idiosyncratic component. In this model, skewness can arise not only from the three components, but also from coskewness due to the higher order covariation between components. An analysis of output growth in 43 U.S. sectors shows that coskewness is a key source of asymmetry in the data and constitutes a connectivity channel not previously explored. To help interpret our results, we construct and estimate a micro-founded multi-sector general equilibrium model and show that it can generate skewness and coskewness consistent with the data.
"The Relationship Between Inflation and the Distribution of Relative Price Changes" (December 2024), with A. Hornstein and A. Wolman.
Monthly U.S. inflation from 1995 through 2019 is well explained by statistics summarizing the monthly distribution of relative price changes. We document this relationship and use it to evaluate the behavior of inflation during and after the COVID-19 pandemic. In earlier periods when inflation was not stable, the relationship between inflation and the distribution of relative price changes shifts, much like the Phillips curve. We use that shifting relationship to derive a measure of underlying inflation that complements existing measures used by central banks.
"Relative Price Shocks and Inflation," (revised: March 2024), with A. Wolman.
Inflation is determined by interaction between monetary policy and real factors, including shocks to supply and demand for different components of the consumption basket. We use a 15-sector New Keynesian model to quantify the contributions to inflation from sectoral supply and demand shocks, monetary policy shocks, and aggregate real shocks. The model is estimated by maximum likelihood on U.S. data from 1995 through 2019, when the policy regime appeared to be stable. Decomposing the 2012-2019 inflation shortfall, and its surge starting in 2021, we find that sectoral shocks were major contributors to the inflation deviations from target.