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Fixed effect estimation of large T panel data models

Ivan Fernandez-Val () and Martin Weidner

No CWP22/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: This article reviews recent advances in fi xed 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 specifi ed 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.

Keywords: panel data; fixed effects; incidental parameter problem; bias correction; unobserved heterogeneity; nonlinear models; jackknife (search for similar items in EconPapers)
Date: 2018-03-28
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https://www.ifs.org.uk/uploads/CWP221818.pdf (application/pdf)

Related works:
Journal Article: Fixed Effects Estimation of Large-TPanel Data Models (2018) Downloads
Working Paper: Fixed Effect Estimation of Large T Panel Data Models (2018) Downloads
Working Paper: Fixed effect estimation of large T panel data models (2017) Downloads
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