Fixed Effects Estimation of Large-TPanel Data Models
By Iv'an Fern'andez-Val, Martin P. Weidner. Annual Review of Economics, 2018
93 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/1c25a076cacee3652010c1784778426de7d90342
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Citations in the corpus
- Two-Step Estimation and Inference with Possibly Many Included Covariates (2018, result): “Our findings are also qualitatively connected to the literature on non-linear panel models with fixed effects (Fernandez-Val and Weidner, 2018, and references therein) in at least two ways.”
- Estimating the effects of non‐discriminatory trade policies within structural gravity models (2017, methodology): “However, in terms of inference, we have nothing to add to Fernández-Val and Weidner (2016, 2018) and Weidner and Zylkin (2019), who focus on and demonstrate consistency, biases and inference of the PPML estimator with alternative sets of fixed effects. and on the exporter side, respectively.5 To…”