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

Hidalgo, Javier and Schafgans, Marcia M. A. (2017) Inference without smoothing for large panels with cross-sectional and temporal dependence. Econometrics (EM597). London School of Economics and Political Science, Suntory and Toyota International Centres for Economics and Related Disciplines, London, UK.

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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 without relying 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 a valid bootstrap which accommodates 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 temporal uncorrelated. Our proposed bootstrap can be viewed as a wild bootstrap in the frequency domain. We present some Monte-Carlo simulations to shed some light on the small sample performance of our inferential procedure and illustrate our results using an empirical example.

Item Type: Monograph (Discussion Paper)
Official URL: http://sticerd.lse.ac.uk/
Additional Information: © 2017 The Authors
Divisions: STICERD
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
Sets: Research centres and groups > Suntory and Toyota International Centres for Economics and Related Disciplines (STICERD)
Date Deposited: 02 May 2018 15:07
Last Modified: 29 Aug 2019 23:27
URI: http://eprints.lse.ac.uk/id/eprint/87748

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