The Correlated Random Effects GMM-Level Estimation: Monte Carlo Evidence and Empirical Applications

Maria Elena Bontempi () and Jan Ditzen ()
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Maria Elena Bontempi: University of Bologna
Jan Ditzen: Free University of Bozen-Bolzano

A chapter in Seven Decades of Econometrics and Beyond, 2025, pp 309-335 from Springer

Abstract: Abstract We introduce CRE-GMM, a new estimator that exploits correlated random effects (CRE) within the generalised method of moments on level equations (GMMlev) in a dynamic (but also static) model on panel data. Unlike GMM-dif, it allows the estimation of the effects of measurable time-invariant covariates and, compared to GMM-sys, makes efficient use of all available information. CRE-GMM considers explanatory variables that may be affected by double endogeneity (correlation with individual heterogeneity and idiosyncratic shocks), models initial conditions and improves inference. Monte Carlo simulations validate CRE-GMM across panel types and endogeneity scenarios. Empirical applications to R&D, production, and wage functions illustrate the advantages of CRE-GMM.

Date: 2025
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DOI: 10.1007/978-3-031-92699-0_11

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