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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.

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

  • 章节

  • 发表日期：

    2025年

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  • Maria Elena Bontempi,
  • Jan Ditzen

摘要

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.