Inference without smoothing for large panels with cross-sectional and temporal dependence

Javier Hidalgo and Marcia Schafgans

Journal of Econometrics, 2021, vol. 223, issue 1, 125-160

Abstract: This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences that do not rely on the choice of any smoothing parameter as is the case with the often employed “HAC” estimator for the covariance matrix. To that end, we propose a cluster estimator for the asymptotic covariance of the estimators and valid bootstrap schemes that do not require the selection of a bandwidth or smoothing parameter and accommodate the nonparametric nature of both temporal and cross-sectional dependence. Our approach is based on the observation that the spectral representation of the fixed effect panel data model is such that the errors become approximately temporally uncorrelated. Our proposed bootstrap schemes can be viewed as wild bootstraps in the frequency domain. We present some Monte Carlo simulations to shed some light on the small sample performance of our inferential procedure.

Keywords: Large panel data models; Cross-sectional strong-dependence; Central limit theorems; Clustering; Discrete Fourier Transformation; Nonparametric bootstrap algorithms (search for similar items in EconPapers)
JEL-codes: C12 C13 C23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:223:y:2021:i:1:p:125-160

DOI: 10.1016/j.jeconom.2020.10.003

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