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Estimation of fixed effects dynamic panel data models: linear differencing or conditional expectation

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  • Cheng Hsiao

Abstract

This note discusses the pros and cons of using the conditional mean approach of Mundlak and Chamberlain and the linear difference approach to deal with the incidental parameters issue in estimating fixed effects dynamic panel data models. The importance of the data generating process of the explanatory variables and the proper treatment of initial values for either approach to get asymptotically unbiased estimators are demonstrated both analytically and through Monte Carlo studies.

Suggested Citation

  • Cheng Hsiao, 2020. "Estimation of fixed effects dynamic panel data models: linear differencing or conditional expectation," Econometric Reviews, Taylor & Francis Journals, vol. 39(8), pages 858-874, September.
    • Handle: RePEc:taf:emetrv:v:39:y:2020:i:8:p:858-874
    DOI: 10.1080/07474938.2020.1772571
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    Cited by:

    1. Maria Elena Bontempi & Jan Ditzen, 2023. "GMM-lev estimation and individual heterogeneity: Monte Carlo evidence and empirical applications," Papers 2312.00399, arXiv.org, revised Dec 2023.

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