My research

Working Papers

• Estimating The Moments and the Distribution of Heterogeneous Marginal Effects Using Panel Data
  [Abstract] [Latest version]

  This paper considers estimation of the moments and the distribution of heterogeneous marginal effects using panel data. We impose no restrictions on the form or dimension of time-invariant heterogeneity. In this setting, we identify the mean, variance, higher-order moments, and the distribution of marginal effects using two periods of data. We propose simple nonparametric estimators for the moments and the distribution, and study their asymptotic properties. The moment estimators are consistent and asymptotically normal. For the distribution estimator, we establish consistency by developing novel results that connect the convergence of distributions to the convergence of their moments. We illustrate the methodology with an application to Engel curves for food at home. Our analysis of variance, higher moments, and the distribution of marginal effects reveals significant heterogeneity. In particular, some households have upward-sloping sections in their Engel curves for lower values of expenditures. In contrast, the average Engel curve is downward-sloping for all expenditure values, in line with the previous literature.

• Inference on Extreme Quantiles of Unobserved Individual Heterogeneity , R&R at Econometric Theory
  [Abstract] [Arxiv][Online appendix]

  We develop a methodology for conducting inference on extreme quantiles of unobserved individual heterogeneity (heterogeneous coefficients, heterogeneous treatment effects, etc.) in a panel data or meta-analysis setting. Inference in such settings is challenging: only noisy estimates of unobserved heterogeneity are available, and approximations based on the central limit theorem work poorly for extreme quantiles. For this situation, under weak assumptions we derive an extreme value theorem and an intermediate order theorem for noisy estimates and appropriate rate and moment conditions. Both theorems are then used to construct confidence intervals for extremal quantiles. The intervals are simple to construct and require no optimization. Inference based on the intermediate order theorem involves a novel self-normalized intermediate order theorem. In simulations, our extremal confidence intervals have favorable coverage properties in the tail. Our methodology is illustrated with an application to firm productivity in denser and less dense areas.

• Unit Averaging for Heterogeneous Panels (with Christian Brownlees) R&R at Journal of Business & Economic Statistics
  [Abstract] [Arxiv][Online appendix][Paper summary in 4 slides][Full slides]

  In this work we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion. We analyze the properties of the minimum MSE unit averaging estimator in a local heterogeneity framework inspired by the literature on frequentist model averaging. The analysis of the estimator covers both the cases in which the cross-sectional dimension of the panel is fixed and large. In both cases, we obtain the local asymptotic distribution of the minimum MSE unit averaging estimators and of the associated weights. A GDP nowcasting application for a panel of European countries showcases the benefits of the procedure.

Work in Progress

• Deconvolution Estimation and Inference on the Distribution of Marginal Effects
• Simple and Powerful Linear Sieves for Conditional Distributions
• Limits of Identification in Nonseparable Nonparametric Models
• Distribution Equality Tests With Noisy Observations (with Andrea Sy)