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Sequential and efficient GMM estimation of dynamic short panel data models

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  • Fei Jin
  • Lung-fei Lee
  • Jihai Yu

Abstract

This paper considers generalized method of moments (GMM) and sequential GMM (SGMM) estimation of dynamic short panel data models. The efficient GMM motivated from the quasi maximum likelihood (QML) can avoid the use of many instrument variables (IV) for estimation. It can be asymptotically efficient as maximum likelihood estimators (MLE) when disturbances are normal, and can be more efficient than QML estimators when disturbances are not normal. The SGMM, which also incorporates many IVs, generalizes the minimum distance estimation originated in Hsiao et al. . By focusing on the estimation of parameters of interest, the SGMM saves computational burden caused by nuisance parameters such as variances of disturbances. It is asymptotically as efficient as the corresponding GMM. In particular, the SGMM based on QML scores can generate a closed-form root estimator for the dynamic parameter, which is asymptotically as efficient as the QML estimator. Nuisance parameters can also be estimated efficiently by an additional SGMM step if they are of interest.

Suggested Citation

  • Fei Jin & Lung-fei Lee & Jihai Yu, 2021. "Sequential and efficient GMM estimation of dynamic short panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 1007-1037, February.
    • Handle: RePEc:taf:emetrv:v:40:y:2021:i:10:p:1007-1037
    DOI: 10.1080/07474938.2021.1889178
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    Cited by:

    1. Tao, Nan & Wu, Tiantian & Yan, Guo, 2024. "To the test of economic recovery: The swings in energy resource prices," Resources Policy, Elsevier, vol. 89(C).
    2. Wei, Xiaobo & Mohsin, Muhammad & Zhang, Qiongxin, 2022. "Role of foreign direct investment and economic growth in renewable energy development," Renewable Energy, Elsevier, vol. 192(C), pages 828-837.

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