Estimation of Panel Data Models with Cross-Sectionally Heteroskedastic Data

48 Pages Posted: 17 Jul 2023

See all articles by Seung C. Ahn

Seung C. Ahn

Arizona State University (ASU) - Economics Department

Xiangyu Zhang

Arizona State University (ASU) - Department of Economics, W.P. Carey School of Business

Date Written: January 15, 2023

Abstract

Panel data models with cross-sectionally heteroskedastic data often suffer from the well-known incidental parameters problem. Some recent studies have proposed that the structural parameters (common parameters to all of the cross-sectional entities) can be consistently estimated if they are estimated jointly with the cross-sectionally weighted averages of the incidental parameters. In this paper, we provide a sufficient condition under which the proposed methods can yield consistent and asymptotically normal estimates of the structural parameters. With the condition, we show that the unrestricted factor IV method proposed by Robertson and Sarafidis (2015, Journal of Econometrics) and the transformed likelihood method of Hayakawa and Pesaran (2015, Journal of Econometrics) can consistently estimate the structural parameters in the panel data models with unknown common factors or dynamic panel models with fixed individual entity-specific effects.

Keywords: Panel data, cross-sectional heteroskedasticity, factor residuals, GMM estimation, transformed MLE

JEL Classification: C13, C23, C51

Suggested Citation

Ahn, Seung C. and Zhang, Xiangyu, Estimation of Panel Data Models with Cross-Sectionally Heteroskedastic Data (January 15, 2023). Available at SSRN: https://ssrn.com/abstract=4503961 or http://dx.doi.org/10.2139/ssrn.4503961

Seung C. Ahn

Arizona State University (ASU) - Economics Department ( email )

Tempe, AZ 85287-3806
United States

Xiangyu Zhang (Contact Author)

Arizona State University (ASU) - Department of Economics, W.P. Carey School of Business ( email )

Tempe, AZ 85287-3706
United States

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