Econometric Analysis of Large Factor Models

Bai, Jushan; Wang, Peng

doi:10.1146/annurev-economics-080315-015356

“…Moon & Weidner (2017) showed that the FE least squares estimator of δ suffers from a Nickell (1981) type incidental parameter bias when the covariates are predetermined, even when the model is correctly specified. We refer to Bai & Wang (2016) for a recent review, and to Pesaran (2006) and Chudik & Pesaran (2015b) for alternative estimation methods of factor models.…”

Section: Multivariate Fixed Effectsmentioning

confidence: 99%

Fixed effect estimation of large T panel data models

Fernández‐Val¹,

Weidner²

2018

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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.

show abstract

“…Moon & Weidner (2017) showed that the FE least squares estimator of δ suffers from a Nickell (1981) type incidental parameter bias when the covariates are predetermined, even when the model is correctly specified. We refer to Bai & Wang (2016) for a recent review on this model.…”

Section: Multivariate Fixed Effectsmentioning

confidence: 99%

Fixed effect estimation of large T panel data models

Fernández‐Val¹,

Weidner²

2017

1

5

0

View full text Add to dashboard Cite

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.

show abstract

“…Moon & Weidner (2017) showed that the FE least squares estimator of δ suffers from a Nickell (1981) type incidental parameter bias when the covariates are predetermined, even when the model is correctly specified. We refer to Bai & Wang (2016) for a recent review, and to Pesaran (2006) and Chudik & Pesaran (2015b) for alternative estimation methods of factor models.…”

Section: Multivariate Fixed Effectsmentioning

confidence: 99%

Fixed Effects Estimation of Large-TPanel Data Models

Fernández‐Val

¹

,

Weidner

²

2018

View full text Add to dashboard Cite

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.

show abstract

Econometric Analysis of Large Factor Models

Cited by 73 publications

References 125 publications

Fixed effect estimation of large T panel data models

Fixed effect estimation of large T panel data models

Fixed effect estimation of large T panel data models

Fixed Effects Estimation of Large-TPanel Data Models

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