Parametric and Nonparametric Frequentist Model Selection and Model Averaging

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, est...

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Main Authors: Aman Ullah, Huansha Wang
Format: Article
Language:English
Published: MDPI AG 2013-09-01
Series:Econometrics
Subjects:
Online Access:http://www.mdpi.com/2225-1146/1/2/157
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author Aman Ullah
Huansha Wang
author_facet Aman Ullah
Huansha Wang
author_sort Aman Ullah
collection DOAJ
description 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.
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spelling doaj.art-40ca104589a547cebf9d5d50d4da7f392022-12-22T04:03:52ZengMDPI AGEconometrics2225-11462013-09-011215717910.3390/econometrics1020157Parametric and Nonparametric Frequentist Model Selection and Model AveragingAman UllahHuansha WangThis 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.http://www.mdpi.com/2225-1146/1/2/157nonparametricmodel selectionmodel averaging
spellingShingle Aman Ullah
Huansha Wang
Parametric and Nonparametric Frequentist Model Selection and Model Averaging
Econometrics
nonparametric
model selection
model averaging
title Parametric and Nonparametric Frequentist Model Selection and Model Averaging
title_full Parametric and Nonparametric Frequentist Model Selection and Model Averaging
title_fullStr Parametric and Nonparametric Frequentist Model Selection and Model Averaging
title_full_unstemmed Parametric and Nonparametric Frequentist Model Selection and Model Averaging
title_short Parametric and Nonparametric Frequentist Model Selection and Model Averaging
title_sort parametric and nonparametric frequentist model selection and model averaging
topic nonparametric
model selection
model averaging
url http://www.mdpi.com/2225-1146/1/2/157
work_keys_str_mv AT amanullah parametricandnonparametricfrequentistmodelselectionandmodelaveraging
AT huanshawang parametricandnonparametricfrequentistmodelselectionandmodelaveraging