Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects

TL;DR

This paper develops a bootstrap-based method for constructing confidence intervals for treatment effects in panel data models with interactive fixed effects, without relying on strict distributional assumptions.

Contribution

It introduces a novel bootstrap approach for valid confidence intervals in interactive fixed effects panel models, applicable with limited post-treatment periods.

Findings

Confidence intervals have asymptotically correct coverage.

Simulation studies show good finite sample performance.

Empirical applications yield consistent treatment effect estimates.

Abstract

We consider the construction of confidence intervals for treatment effects estimated using panel models with interactive fixed effects. We first use the factor-based matrix completion technique proposed by Bai and Ng (2021) to estimate the treatment effects, and then use bootstrap method to construct confidence intervals of the treatment effects for treated units at each post-treatment period. Our construction of confidence intervals requires neither specific distributional assumptions on the error terms nor large number of post-treatment periods. We also establish the validity of the proposed bootstrap procedure that these confidence intervals have asymptotically correct coverage probabilities. Simulation studies show that these confidence intervals have satisfactory finite sample performances, and empirical applications using classical datasets yield treatment effect estimates of similar magnitudes and reliable confidence intervals.