Publication Type

Journal Article

Publication Date

10-2015

Abstract

This paper considers model selection in panels where incidental parameters are present. Primary interest centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters. Modifications are then needed to achieve consistency. New model selection information criteria are developed here that use either the Kullback-Leibler information criterion based on the profile likelihood or the Bayes factor based on the integrated likelihood with a bias-reducing prior. These model selection criteria impose heavier penalties than those associated with standard information criteria such as AIC and BIC. The additional penalty, which is data-dependent, properly reflects the model complexity arising from the presence of incidental parameters. A particular example is studied in detail involving lag order selection in dynamic panel models with fixed effects. The new criteria are shown to control for over/under-selection probabilities in these models and lead to consistent order selection criteria. (C) 2015 Elsevier B.V. All rights reserved.

Keywords

(Adaptive) model selection, Incidental parameters, Profile likelihood, Kullback-Leibler information, Integrated likelihood, Bias-reducing prior, Fixed effects, Lag order

Discipline

Econometrics

Research Areas

Econometrics

Publication

Journal of Econometrics

Volume

188

Issue

2

First Page

474

Last Page

489

ISSN

0304-4076

Identifier

10.1016/j.jeconom.2015.03.012

Publisher

Elsevier: 24 months

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

https://doi.org/10.1016/j.jeconom.2015.03.012

Included in

Econometrics Commons

Share

COinS