Economists are often interested in estimating averages with respect to distributions of unobservables, such as moments of individual fixed-effects, or average partial effects in discrete choice models.
This paper describes three methods for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems.
How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status.
This paper proposes a powerful alternative to the t-test in linear regressions when a regressor is mismeasured. We assume there is a second contaminated measurement of the regressor of interest.
In this paper, we consider the problem of accounting for such uncertainty by constructing confidence sets for the rank of each population. We consider both the problem of constructing marginal confidence sets for the rank of a particular population as well as simultaneous confidence sets for the ranks of all populations.
We study the incidental parameter problem for the “three-way” Poisson Pseudo-Maximum Likelihood (PPML) estimator recently recommended for identifying the effects of trade policies and in other panel data gravity settings.
We study an extension of a treatment effect model in which an observed discrete classifier indicates which one of a set of counterfactual processes occurs, each of which may result in the realization of several endogenous outcomes.
Estimates of how health affects employment vary considerably. We assess how different methods and health measures impact estimates of the impact of health on employment using a unified framework for the US and England.
We study testable implications of multiple equilibria in discrete games with incomplete information. Unlike de Paula and Tang (2012), we allow the players’ private signals to be correlated.
We provide estimation methods for panel nonseparable models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data.