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FDML versus GMM for Dynamic Panel Models with Roots Near Unity

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  • Adrian Mehic

    (Department of Economics, Lund University, SE-223 63 Lund, Sweden)

Abstract

This paper evaluates the first-differenced maximum likelihood (FDML) and the continuously updating system generalized method of moments (CU-GMM) estimators of dynamic panel models when the data is close to non-stationary. This case is far from trivial, as a high degree of persistence is the norm rather than the exception in economic panels, particularly in financial management. While the CU-GMM is shown to have lower bias and higher power, it suffers from severe size distortions, which are exacerbated when the data approaches non-stationarity.

Suggested Citation

  • Adrian Mehic, 2021. "FDML versus GMM for Dynamic Panel Models with Roots Near Unity," JRFM, MDPI, vol. 14(9), pages 1-9, August.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:9:p:405-:d:622597
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    References listed on IDEAS

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