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Fixed effect estimation of large T panel data models

Authors: Ivan Fernandez-Val and Martin Weidner
Date: 03 October 2017
Type: cemmap Working Paper, CWP42/17
DOI: 10.1920/wp.cem.2017.4217


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.

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Ivan Fernandez-Val and Martin Weidner March 2018, Fixed effect estimation of large T panel data models, cemmap Working Paper, CWP22/18, The IFS

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