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Fixed effect estimation of large T panel data models

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  • Ivan Fernandez-Val

    (Institute for Fiscal Studies and Boston University)

  • Martin Weidner

    (Institute for Fiscal Studies and University College London)

Abstract

This article reviews recent advances in ?xed e?ect 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 e?ects, where the distribution of the outcome variable conditional on covariates and unobserved e?ects is speci?ed parametrically, while the distribution of the unobserved e?ects 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 e?ects, split-panel Jackknife bias corrections, unbalanced panels, distribution and quantile e?ects, and other extensions. Understanding and correcting the incidental parameter bias caused by the estimation of many ?xed e?ects 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 CWP42/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:42/17
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    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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