"Relative Price Shocks and Inflation," (revised: May 2026), with A. Wolman. Prepared for the Carnegie-Rochester-NYU Conference Series on Public Policy.
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 construct a 15-sector New Keynesian model with a production network and heterogeneity in price rigidity, in the volatility of sectoral shocks, and in the trend rates of productivity growth across sectors 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. We find that relative price shocks were major contributors to the inflation deviations from target and the inflation surge after the pandemic
"Skewed Fluctuations and Propagation Through Production Networks" (revised: July 2026), with K. Kamepalli and S. Ng. Revise and resubmit requested by Quantitative Economics.
Aggregate skewness may arise exogenously because common shocks are skewed, or endogenously from the asymmetric propagation of symmetric sectoral shocks through the production network. These two possibilities are often considered in isolation by previous theoretical work. To evaluate the relative importance of these channels in the data, we decompose output growth into i) a common component, ii) an idiosyncratic component, and iii) a network component that propagates variations of the first two components through the production network. We use the framework to assess the sources of skewness and quantify network spillovers, while allowing the input-output matrix to be an inexact measure of connectivity. Our analysis of data for 43 sectors in the U.S. finds that spillovers can be traced to a few industries, and the network component is significantly more skewed than the common component. However, the coskewness between the network and the common component is the most important source of asymmetry in aggregate output growth. To help understand this channel of skewness that has previously received little attention in the literature, we construct and estimate a production network model with possibly non-Gaussian sectoral and common shocks, and show that it too displays skewness and coskewness consistent with the data.
"Membership Turnover and Policy Disagreement at the FOMC" (August 2025), with A. Riboni. Revise and resubmit requested by AEJ: Macroeconomics.
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.
"Deliberation and Policy Outcomes: Evidence from the Textual Analysis of FOMC Transcripts" (November 2025), with A. Riboni and L. Tran.
Natural language processing is used to extract information from FOMC transcripts and construct quantitative text-based measures of voiced policy stance, emotions, and collaboration. These measures are inputs in an econometric model of deliberation where members interact with one another across rounds of a meeting and over time across meetings. Evidence shows that members learn from one another during within-meeting deliberation and exert influence across meetings. Although emotional tone has limited effects on policy stances and decisions, it has strong predictive power for dissent behavior.
"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."
"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.