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

Hidalgo, Javier and Schafgans, Marcia (2020) Inference without smoothing for large panels with cross-sectional and temporal dependence. Journal of Econometrics. ISSN 0304-4076

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Identification Number: 10.1016/j.jeconom.2020.10.003

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.

Item Type: Article
Official URL: https://www.sciencedirect.com/journal/journal-of-e...
Additional Information: © 2020 Elsevier B.V.
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C12 - Hypothesis Testing
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C13 - Estimation
C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C23 - Models with Panel Data
Date Deposited: 11 Nov 2020 16:33
Last Modified: 17 Feb 2021 17:25
URI: http://eprints.lse.ac.uk/id/eprint/107426

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