Fixed Effects Estimation of Large-TPanel Data Models

Ivan Fernandez-Val and Martin Weidner

Annual Review of Economics, 2018, vol. 10, issue 1, 109-138

Abstract: This article reviews recent advances in fixed effects 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. In contrast to existing reviews on long panels, 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 are our main focuses, 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 being the number of estimated parameters and n the total sample size.

Keywords: bias correction; fixed effects; incidental parameter problem; nonlinear models; panel data; unobserved heterogeneity (search for similar items in EconPapers)
JEL-codes: C23 C33 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Fixed Effect Estimation of Large T Panel Data Models (2018)
Working Paper: Fixed effect estimation of large T panel data models (2018)
Working Paper: Fixed effect estimation of large T panel data models (2017)
Working Paper: Fixed effect estimation of large T panel data models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:anr:reveco:v:10:y:2018:p:109-138

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