LIU, CHUAN (2012): A plugin averaging estimator for regressions with heteroskedastic errors.

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Abstract
This paper proposes a new model averaging estimator for the linear regression model with heteroskedastic errors. We address the issues of how to optimally assign the weights for candidate models and how to make inference based on the averaging estimator. We derive the asymptotic mean squared error (AMSE) of the averaging estimator in a local asymptotic framework, and then choose the optimal weights by minimizing the AMSE. We propose a plugin estimator of the optimal weights and use these estimated weights to construct a plugin averaging estimator of the parameter of interest. We derive the asymptotic distribution of the plugin averaging estimator and suggest a plugin method to construct confidence intervals. Monte Carlo simulations show that the plugin averaging estimator has much smaller expected squared error, maximum risk, and maximum regret than other existing model selection and model averaging methods. As an empirical illustration, the proposed methodology is applied to crosscountry growth regressions.
Item Type:  MPRA Paper 

Original Title:  A plugin averaging estimator for regressions with heteroskedastic errors 
Language:  English 
Keywords:  Local asymptotic theory, Model averaging, Model selection, Plugin estimators 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C51  Model Construction and Estimation C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection 
Item ID:  41414 
Depositing User:  CHUAN LIU 
Date Deposited:  18. Sep 2012 13:52 
Last Modified:  10. Oct 2015 02:41 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/41414 