Indirect Inference for Nonlinear Panel Models with Fixed Effects

TL;DR

This paper introduces a simulation-based indirect inference method to reduce bias in nonlinear fixed effects panel data models, improving estimate accuracy and confidence interval coverage.

Contribution

It develops a novel bias correction technique using simulated data to address the incidental parameter problem in nonlinear panel models with fixed effects.

Findings

Method reduces bias in fixed effect estimators

Improves confidence interval coverage

Demonstrated effectiveness through application and simulations

Abstract

Fixed effect estimators of nonlinear panel data models suffer from the incidental parameter problem. This leads to two undesirable consequences in applied research: (1) point estimates are subject to large biases, and (2) confidence intervals have incorrect coverages. This paper proposes a simulation-based method for bias reduction. The method simulates data using the model with estimated individual effects, and finds values of parameters by equating fixed effect estimates obtained from observed and simulated data. The asymptotic framework provides consistency, bias correction, and asymptotic normality results. An application and simulations to female labor force participation illustrates the finite-sample performance of the method.