Improved Inference for Interactive Fixed Effects Model under Cross-Sectional Dependence

Zhenhao Gong and Min Seong Kim
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Zhenhao Gong: Shanxi University of Finance and Economics

No 2024-02, Working papers from University of Connecticut, Department of Economics

Abstract: This paper proposes an inference procedure for the interactive fixed effects model that is valid in the presence of cross-sectional dependence. When the error terms are cross-sectionally dependent, the Least Square (LS) estimator of this model is asymptotically biased and therefore the associated confidence interval tends to have a large coverage error. To address this, we propose a bias correction of the LS estimator and a cross-sectional dependence robust variance estimator to construct associated test statistics. The paper also discusses practical issues in implementing the proposed method, including the construction of distance that reflects the decaying pattern of cross-sectional dependence and the selection of the bandwidth parameters. Monte Carlo simulations show our procedure works well in finite samples. As empirical illustrations, we apply our procedure to study the effect of divorce law reforms on divorce rates and the impact of clean water and sewerage interventions on child mortality.

Keywords: Bandwidth selection; Bias correction; Robust inference; Spatial HAC method (search for similar items in EconPapers)
JEL-codes: C12 C13 C15 C31 C33 I18 J12 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2024-02
New Economics Papers: this item is included in nep-ecm
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