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

Content Provider	Springer Nature Link
Author	Huang, Jiewu Yang, Hu
Copyright Year	2013
Abstract	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.
Ending Page	230
Page Count	14
Starting Page	217
File Format	PDF
ISSN	09325026
e-ISSN	16139798
Journal	Statistical Papers
Issue Number	1
Volume Number	56
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-12-21
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mean squared error matrix Ridge regression; shrinkage estimators Linear regression Statistics for Business/Economics/Mathematical Finance/Insurance Operations Research/Decision Theory Multicollinearity Generalized least squares estimator Probability Theory and Stochastic Processes Economic Theory Principal component two-parameter estimator Autocorrelation
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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