Average and Quantile Effects in Nonseparable Panel Models

TL;DR

This paper develops identification, estimation, and inference methods for nonseparable panel models, addressing average and quantile effects, and providing solutions for dynamic models and semiparametric bounds with empirical applications.

Contribution

It introduces new estimators and bounds for average and quantile effects in nonseparable panel models, including dynamic and semiparametric cases, with computational methods and inference procedures.

Findings

The fixed-effects estimator is inconsistent for average effects.

A new consistent estimator for average effects is proposed.

Semiparametric bounds can be tighter than nonparametric bounds.

Abstract

Nonseparable panel models are important in a variety of economic settings, including discrete choice. This paper gives identification and estimation results for nonseparable models under time homogeneity conditions that are like "time is randomly assigned" or "time is an instrument." Partial identification results for average and quantile effects are given for discrete regressors, under static or dynamic conditions, in fully nonparametric and in semiparametric models, with time effects. It is shown that the usual, linear, fixed-effects estimator is not a consistent estimator of the identified average effect, and a consistent estimator is given. A simple estimator of identified quantile treatment effects is given, providing a solution to the important problem of estimating quantile treatment effects from panel data. Bounds for overall effects in static and dynamic models are given. The dynamic bounds provide a partial identification solution to the important problem of estimating the effect of state dependence in the presence of unobserved heterogeneity. The impact of $T$, the number of time periods, is shown by deriving shrinkage rates for the identified set as $T$ grows. We also consider semiparametric, discrete-choice models and find that semiparametric panel bounds can be much tighter than nonparametric bounds. Computationally-convenient methods for semiparametric models are presented. We propose a novel inference method that applies in panel data and other settings and show that it produces uniformly valid confidence regions in large samples. We give empirical illustrations.