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.
-
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.
- Monetary Policy According to Data
- Distributional Counterfactuals
- Job Levels II
- Unequal Barrels and Slick Consequences: The Distributional Impact of Oil Shocks