Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/103630
Authors: 
Ullah, Aman
Wang, Huansha
Year of Publication: 
2013
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Publisher:] MDPI [Place:] Basel [Volume:] 1 [Year:] 2013 [Issue:] 2 [Pages:] 157-179
Abstract: 
This paper presents recent developments in model selection and model averaging for parametric and nonparametric models. While there is extensive literature on model selection under parametric settings, we present recently developed results in the context of nonparametric models. In applications, estimation and inference are often conducted under the selected model without considering the uncertainty from the selection process. This often leads to inefficiency in results and misleading confidence intervals. Thus an alternative to model selection is model averaging where the estimated model is the weighted sum of all the submodels. This reduces model uncertainty. In recent years, there has been significant interest in model averaging and some important developments have taken place in this area. We present results for both the parametric and nonparametric cases. Some possible topics for future research are also indicated.
Subjects: 
nonparametric
model selection
model averaging
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article
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