Model Selection in the Presence of Incidental Parameters
AbstractThis paper considers model selection in nonlinear panel data models where incidental parameters or large-dimensional nuisance parameters are present. Primary interest typically centers 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 datadependent, 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/underselection probabilities in these models and lead to consistent order selection criteria.
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Bibliographic InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 159.
Length: 41 pages
Date of creation: Oct 2013
Date of revision:
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(Adaptive) model selection; incidental parameters; profile likelihood; Kullback- Leibler information; Bayes factor; integrated likelihood; robust prior; model complexity; fixed effects; lag order;
Other versions of this item:
- Yoonseok Lee & Peter C.B. Phillips, 2013. "Model Selection in the Presence of Incidental Parameters," Cowles Foundation Discussion Papers 1919, Cowles Foundation for Research in Economics, Yale University.
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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