Estimation of heterogeneous panels with systematic slope variations

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. (c) 2020 Elsevier B.V. All rights reserved.

Automated summary

The research shows that different estimation methods exhibit different biases and consistencies in the presence of systematic slope variation. The simple and robust version of the pooled estimator, obtained through the augmented regression method, avoids large standard errors of the mean-group estimator in cases of small time periods.