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

In: Seven Decades of Econometrics and Beyond

Author

Listed:
  • Maria Elena Bontempi

    (University of Bologna)

  • Jan Ditzen

    (Free University of Bozen-Bolzano)

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.

Suggested Citation

  • Maria Elena Bontempi & Jan Ditzen, 2025. "The Correlated Random Effects GMM-Level Estimation: Monte Carlo Evidence and Empirical Applications," Advanced Studies in Theoretical and Applied Econometrics, in: Badi H. Baltagi & László Mátyás (ed.), Seven Decades of Econometrics and Beyond, pages 309-335, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-92699-0_11
    DOI: 10.1007/978-3-031-92699-0_11
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