A Simple Bootstrap Method for Panel Data Inferences

Jiti Gao (), Bin Peng () and Yayi Yan ()

No 7/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we propose a simple dependent wild bootstrap procedure for us to establish valid inferences for a wide class of panel data models including those with interactive fixed effects. The proposed method allows for the error components having weak correlation over both dimensions, and heteroskedasticity. The asymptotic properties are established under a set of simple and general conditions, and bridge the literature on bootstrap methods and the literature of heteroskedasticity and autocorrlation consistent (HAC) approaches for panel data models. The new findings fill some gaps left by the bulk literature of the block bootstrap based panel data studies. Finally, we show the superiority of our approach over several natural competitors using extensive numerical studies.

Keywords: Cross-sectional dependence; Edgeworth expansion; panel data bootstrap; time series autocorrelation (search for similar items in EconPapers)
JEL-codes: C12 C18 C23 (search for similar items in EconPapers)
Pages: 67
Date: 2022
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Citations: View citations in EconPapers (2)

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