Weighted average least squares estimation with nonspherical disturbances and an application to the Hong Kong housing market

被引：31

作者：

Magnus, Jan R. ^{[2
]}

Wan, Alan T. K. ^{[1
]}

Zhang, Xinyu ^{[3
]}

机构：

[1] City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China

[2] Tilburg Univ, Dept Econometr & Operat Res, Tilburg, Netherlands

[3] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2011年 / 55卷 / 03期

关键词：

Model averaging; Bayesian analysis; Monte Carlo; Housing demand; MODEL-SELECTION ESTIMATORS; LINEAR-REGRESSION MODELS; VARIABLE SELECTION; PRICE MODELS; INFORMATION; INFERENCE;

D O I：

10.1016/j.csda.2010.09.023

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The recently proposed weighted average least squares (WALS) estimator is a Bayesian combination of frequentist estimators It has been shown that the WALS estimator possesses major advantages over standard Bayesian model averaging (BMA) estimators the WALS estimator has bounded risk allows a coherent treatment of ignorance and its computational effort is negligible However the sampling properties of the WALS estimator as compared to BMA estimators are heretofore unexamined The WALS theory is further extended to allow for nonspherical disturbances and the estimator is illustrated with data from the Hong Kong real estate market Monte Carlo evidence shows that the WALS estimator performs significantly better than standard BMA and pretest alternatives (C) 2010 Elsevier B V All rights reserved

引用

页码：1331 / 1341

页数：11

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