Robust estimation of large panels with factor structures

Avarucci, M. and Zaffaroni, P. (2022) Robust estimation of large panels with factor structures. Journal of the American Statistical Association, (doi: 10.1080/01621459.2022.2050244) (Early Online Publication)

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Abstract

This paper studies estimation of linear panel regression models with heterogeneous coefficients using a class of weighted least squares estimators, when both the regressors and the error possibly contain a common latent factor structure. Our theory is robust to the specification of such a factor structure because it does not require any information on the number of factors or estimation of the factor structure itself. Moreover, our theory is efficient, in certain circumstances, because it nests the GLS principle. We first show how our unfeasible weighted-estimator provides a bias-adjusted estimator with the conventional limiting distribution, for situations in which the OLS is affected by a first-order bias. The technical challenge resolved in the paper consists of showing how these properties are preserved for the feasible weighted estimator in a double-asymptotics setting. Our theory is illustrated by extensive Monte Carlo experiments and an empirical application that investigates the link between capital accumulation and economic growth in an international setting.

Item Type:Articles
Status:Early Online Publication
Refereed:Yes
Glasgow Author(s) Enlighten ID:Avarucci, Dr Marco
Authors: Avarucci, M., and Zaffaroni, P.
College/School:College of Social Sciences > Adam Smith Business School > Economics
Journal Name:Journal of the American Statistical Association
Publisher:Taylor & Francis
ISSN:0162-1459
ISSN (Online):1537-274X
Published Online:11 April 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Journal of the American Statistical Association 2022
Publisher Policy:Reproduced under a Creative Commons License

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