Estimation of heterogeneous panels with systematic slope variations

Jörg Breitung () and Nazarii Salish

Journal of Econometrics, 2021, vol. 220, issue 2, 399-415

Abstract: We analyse estimation procedures for the panel data models with heterogeneous slopes. Specifically we take into account a possible dependence between regressors and heterogeneous slope coefficients, which is referred to as systematic variation. It is shown that under relevant forms of systematic slope variations (i) the pooled OLS estimator is severely biased, (ii) Swamy’s GLS estimator is inconsistent if the number of time periods T is fixed, whereas (iii) the mean-group estimator always provides consistent estimators at the risk of high variances. Following Mundlak (1978) we propose an augmentated regression which results in a simple and robust version of the pooled estimator. The latter approach avoids the risk of large standard errors of the mean-group estimator, whenever T is small. We also propose two test statistics for systematic slope variation using the Lagrange multiplier and Hausman principles. We derive their asymptotic properties and provide a local power analysis of both test statistics. Monte Carlo experiments corroborate our theoretical findings and show that for all combinations of N and T the Mundlak-type GLS estimator outperform all other estimators.

Keywords: Panel data; Random effects; Slope heterogeneity (search for similar items in EconPapers)
JEL-codes: C13 C23 C33 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:220:y:2021:i:2:p:399-415

DOI: 10.1016/j.jeconom.2020.04.007

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