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Autocovariance based estimation in the linear regression model  

Park, Cheol-Yong (Department of Statistics, Keimyung University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.5, 2011 , pp. 839-847 More about this Journal
Abstract
In this study, we derive an estimator based on autocovariance for the regression coefficients vector in the multiple linear regression model. This method is suggested by Park (2009), and although this method does not seem to be intuitively attractive, this estimator is unbiased for the regression coefficients vector. When the vectors of exploratory variables satisfy some regularity conditions, under mild conditions which are satisfied when errors are from autoregressive and moving average models, this estimator has asymptotically the same distribution as the least squares estimator and also converges in probability to the regression coefficients vector. Finally we provide a simulation study that the forementioned theoretical results hold for small sample cases.
Keywords
Autocovariace method; least squares method; multiple linear regression;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
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