Research
-
Distributional Dynamics
We develop a new method for deriving high-frequency synthetic distributions of consumption, income, and wealth. Modern theories of macroeconomic dynamics identify the joint distribution of consumption, income, and wealth as a key determinant of aggregate dynamics. Our novel method allows us to study their distributional dynamics over time. The method can incorporate different microdata sources, regardless of their frequency and coverage of variables, to generate high-frequency synthetic distributional data. We extend existing methods by allowing for more flexible data inputs. The core of the method is to treat the distributional data as a time series of functions whose underlying factor structure follows a state-space model, which we estimate using Bayesian techniques. We show that the novel method provides the high-frequency distributional data needed to understand better the dynamics of consumption and its distribution over the business cycle.
-
Not All Oil Supply Shocks are Alike: The Macroeconomic and Distributional Impact of Oil Supply Shocks
Oil supply shocks shape U.S. inequality, and the type of oil supply shock matters for who bears the cost. In a Bayesian VAR that embeds the joint distribution of U.S. household income, consumption, and wealth alongside a standard oil-market block, sudden production shortfalls (Baumeister and Hamilton, 2019) raise inequality, whereas oil supply news (Känzig, 2021) mostly reduces it. These differences are not visible in the aggregate dynamics, which appear similar across shocks. A causal mediation analysis (Dufour and Wang, 2024) of these aggregate dynamics shows these shocks operate through distinct channels and it is this mechanism wedge, rather than the aggregate footprint, that determines who bears the cost. The analysis shares the view of Kilian and Lewis (2011) that policy responses should depend on the underlying causes of oil price shocks.
-
Oil Supply Shocks and Monetary Policy
This paper documents the role of monetary policy in the transmission of oil supply shocks to the U.S. macroeconomy and to the joint distribution of household income, consumption, and wealth. A single Bayesian VAR identifies a Känzig (2021) oil supply news shock alongside monetary policy shocks that span the entire policy spectrum — the short rate, forward guidance, and the balance sheet. The causal mediation framework of Dufour and Wang (2024) recovers which channels the Federal Reserve's reaction function loads on; the sufficient-statistics counterfactual of Caravello, McKay and Wolf (2024) evaluates a nominal rate peg, strict CPI-inflation targeting, and an optimal dual-mandate rule. At short constraint horizons, aggregate responses are similar across rules; differences emerge with the duration over which a rule is enforced, and in the cross-section at all horizons, where strict inflation targeting compresses income, consumption, and wealth inequality relative to the realized path, driven by gains at the bottom of the distribution. Because every distributional statistic is a linear functional of the system, the framework extends to a spectrum of rules with explicitly distributional objectives — from a single mandate that targets inequality alone, to a triple mandate that weighs it against inflation and unemployment, to a constraint protecting the bottom half of the consumption distribution. Companion exercises assess the credibility of the counterfactuals under departures from full-information rational expectations and compare the policy cost of the rules across oil-shock types — belief-driven supply news versus realized physical shortfalls.
-
Spatial standard errors for several commonly used M-estimators
We provide a unified implementation of existing asymptotic theory that computes Conley-style spatial HAC standard errors for a wide range of commonly used (non-linear) estimators. We cover OLS, logit, probit, Poisson, and negative binomial regressions, as well as the fixed-effects estimators areg and reghdfe—extending commonly used publicly available routines (currently limited to linear models) to nonlinear M-estimators and fixed-effects workflows. We provide Stata and Python software implementing the procedure.
Works in progress
- Distributional Dynamics: Germany
- Distributional Counterfactuals
- Monetary Policy According to Data
- Sectoral Job Levels and Productivity
- The Fiscal Roots of Wealth Inequality: Corporate Concentration and the Eroding Tax Base