Two-Way Mean Group Estimators for Heterogeneous Panel Models with Fixed T

TL;DR

This paper introduces a novel two-way mean group estimator for heterogeneous panel models with fixed T, along with a jackknife inference method, and demonstrates its effectiveness through simulations and real data applications.

Contribution

It develops a new estimator and inference procedure for two-way fixed effects models with heterogeneous slopes in a fixed T setting, including a Hausman-type test for poolability.

Findings

Estimator performs well in finite samples.

Jackknife inference provides valid confidence intervals.

Applications reveal meaningful relationships in health and production data.

Abstract

We consider a correlated random coefficient panel data model with two-way fixed effects and interactive fixed effects in a fixed T framework. We propose a two-way mean group (TW-MG) estimator for the expected value of the slope coefficient and propose a leave-one-out jackknife method for valid inference. We also consider a pooled estimator and provide a Hausman-type test for poolability. Simulations demonstrate the excellent performance of our estimators and inference methods in finite samples. We apply our new methods to two datasets to examine the relationship between health-care expenditure and income, and estimate a production function.