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Fixed effect estimation of large T panel data models

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  • Ivan Fernandez-Val
  • Martin Weidner

Abstract

This article reviews recent advances in fixed effect estimation of panel data models for long panels, where the number of time periods is relatively large. We focus on semiparametric models with unobserved individual and time effects, where the distribution of the outcome variable conditional on covariates and unobserved effects is specified parametrically, while the distribution of the unobserved effects is left unrestricted. Compared to existing reviews on long panels (Arellano & Hahn, 2007; a section in Arellano & Bonhomme, 2011) we discuss models with both individual and time effects, split-panel Jackknife bias corrections, unbalanced panels, distribution and quantile effects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many fixed effects is our main focus, and the unifying theme is that the order of this bias is given by the simple formula p/n for all models discussed, with p the number of estimated parameters and n the total sample size.

Suggested Citation

  • Ivan Fernandez-Val & Martin Weidner, 2017. "Fixed effect estimation of large T panel data models," CeMMAP working papers 42/17, Institute for Fiscal Studies.
    • Handle: RePEc:azt:cemmap:42/17
    DOI: 10.1920/wp.cem.2017.4217
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    References listed on IDEAS

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    Cited by:

    1. Xuan Leng & Jiaming Mao & Yutao Sun, 2023. "Debiased Inference for Dynamic Nonlinear Panels with Multi-dimensional Heterogeneities," Papers 2305.03134, arXiv.org, revised Nov 2024.

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