Model selection in the presence of incidental parameters
Model selection in the presence of incidental parameters
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.
474-489
Lee, Yoonseok
808cdb2d-6cc3-4c29-9347-6dede7994116
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
1 October 2015
Lee, Yoonseok
808cdb2d-6cc3-4c29-9347-6dede7994116
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Lee, Yoonseok and Phillips, Peter C.B.
(2015)
Model selection in the presence of incidental parameters.
Journal of Econometrics, 188 (2), .
(doi:10.1016/j.jeconom.2015.03.012).
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.
This record has no associated files available for download.
More information
e-pub ahead of print date: 12 March 2015
Published date: 1 October 2015
Organisations:
Economics
Identifiers
Local EPrints ID: 407665
URI: http://eprints.soton.ac.uk/id/eprint/407665
ISSN: 0304-4076
PURE UUID: afc054ac-13ee-4aca-9075-23bc150745ae
Catalogue record
Date deposited: 21 Apr 2017 01:03
Last modified: 15 Mar 2024 12:45
Export record
Altmetrics
Contributors
Author:
Yoonseok Lee
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics