Robust Estimation of Average Treatment Effects from Panel Data

TL;DR

This paper introduces a robust estimator for the average treatment effect in panel data, addressing robustness issues of existing methods, with theoretical analysis, simulations, and real-world application to economic impact assessment.

Contribution

It proposes a new robust ATE estimator using minimum density power divergence, with comprehensive theoretical, simulation, and empirical validation.

Findings

The estimator is robust to data contamination.

Simulation results show improved finite-sample performance.

Application reveals significant long-term economic effects of the tsunami.

Abstract

In order to evaluate the impact of a policy intervention on a group of units over time, it is important to correctly estimate the average treatment effect (ATE) measure. Due to lack of robustness of the existing procedures of estimating ATE from panel data, in this paper, we introduce a robust estimator of the ATE and the subsequent inference procedures using the popular approach of minimum density power divergence inference. Asymptotic properties of the proposed ATE estimator are derived and used to construct robust test statistics for testing parametric hypotheses related to the ATE. Besides asymptotic analyses of efficiency and powers, extensive simulation studies are conducted to study the finite-sample performances of our proposed estimation and testing procedures under both pure and contaminated data. The robustness of the ATE estimator is further investigated theoretically through the influence functions analyses. Finally our proposal is applied to study the long-term economic effects of the 2004 Indian Ocean earthquake and tsunami on the (per-capita) gross domestic products (GDP) of five mostly affected countries, namely Indonesia, Sri Lanka, Thailand, India and Maldives.