Publication Date: October 2013
This paper considers model selection in nonlinear panel data models where incidental parameters or large-dimensional nuisance parameters are present. Primary interest typically centres on selecting a model that best approximates the underlying structure involving parameters that are common within the panel after concentrating out the incidental parameters. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters (Han et al., 2012). 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 the robust prior of Arellano and Bonhomme (2009). 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 individual eﬀects. The new criteria are shown to control for over/under-selection probabilities in these models and lead to consistent order selection criteria.
(Adaptive) model selection, Incidental parameters, Proﬁle likelihood, Kullback-Leibler information, Bayes factor, Integrated likelihood, Robust prior, Model complexity, Fixed eﬀects, Lag order
JEL Classification Codes: C23, C52
See CFP: 1494