Robust estimation of large panels with factor structures

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Title: Robust estimation of large panels with factor structures
Authors: Avarucci, M
Zaffaroni, P
Item Type: Journal Article
Abstract: This article 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 article 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. Supplementary materials for this article are available online.
Issue Date: 2-Apr-2022
Date of Acceptance: 9-Feb-2022
URI: http://hdl.handle.net/10044/1/97465
DOI: 10.1080/01621459.2022.2050244
ISSN: 0162-1459
Publisher: Taylor and Francis
Journal / Book Title: Journal of the American Statistical Association
Copyright Statement: © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Factor structure
GLS
Panel
Robustness
Weighted least squares estimation
REGRESSION-MODELS
INFERENCE
GROWTH
ERROR
Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Factor structure
GLS
Panel
Robustness
Weighted least squares estimation
REGRESSION-MODELS
INFERENCE
GROWTH
ERROR
Statistics & Probability
0104 Statistics
1403 Econometrics
1603 Demography
Publication Status: Published online
Online Publication Date: 2022-03-09
Appears in Collections:Imperial College Business School



This item is licensed under a Creative Commons License Creative Commons