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Parametric and Nonparametric Frequentist Model Selection and Model Averaging
Department of Economics, University of California, Riverside, CA 92521-0427, USA
* Author to whom correspondence should be addressed.
Received: 27 June 2013; in revised form: 17 July 2013 / Accepted: 13 September 2013 / Published: 20 September 2013
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.
Keywords: nonparametric; model selection; model averaging
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Cite This Article
MDPI and ACS Style
Ullah, A.; Wang, H. Parametric and Nonparametric Frequentist Model Selection and Model Averaging. Econometrics 2013, 1, 157-179.
Ullah A, Wang H. Parametric and Nonparametric Frequentist Model Selection and Model Averaging. Econometrics. 2013; 1(2):157-179.
Ullah, Aman; Wang, Huansha. 2013. "Parametric and Nonparametric Frequentist Model Selection and Model Averaging." Econometrics 1, no. 2: 157-179.