On a principal component two-parameter estimator in linear model with autocorrelated errors

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• 作者：Jiewu Huang (1)
  Hu Yang (1)
  
  1. College of Mathematics and Statistics ; Chongqing University ; Chongqing ; 401331 ; China
  
• 关键词：Autocorrelation ; Multicollinearity ; Principal component two ; parameter estimator ; Generalized least squares estimator ; Mean squared error matrix ; 62J05 ; 62J07
• 刊名：Statistical Papers
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：56
• 期：1
• 页码：217-230
• 全文大小：174 KB
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• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics for Business, Economics, Mathematical Finance and Insurance
  Probability Theory and Stochastic Processes
  Economic Theory
  Operation Research and Decision Theory
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1613-9798

文摘

This paper is concerned with autocorrelation in errors and multicollinearity among the regressors in linear regression model. To reduce these effects of autocorrelation and multicollinearity, we generalize a principal component two-parameter (PCTP) estimator in the linear regression model with correlated or heteroscedastic errors. Then we give detailed comparisons between those estimators that can be derived from the PCTP estimator such as the generalized least squares estimator, the principal components regression estimator, the \(r-k\) estimator and the \(r-d\) estimator by the mean squared error (MSE) matrix criterion. Also, we obtain the conditions for the superiority of one estimator over the other. Furthermore, we conduct a Monte Carlo simulation study to compare these estimators under the MSE criterion.