A stochastic restricted ridge regression estimator


Ozkale M. R.

JOURNAL OF MULTIVARIATE ANALYSIS, vol.100, no.8, pp.1706-1716, 2009 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 100 Issue: 8
  • Publication Date: 2009
  • Doi Number: 10.1016/j.jmva.2009.02.005
  • Title of Journal : JOURNAL OF MULTIVARIATE ANALYSIS
  • Page Numbers: pp.1706-1716
  • Keywords: Multicollinearity, Restricted ridge regression estimator, Stochastic linear restrictions, Mixed estimator, Autocorrelated error, LAGGED DEPENDENT-VARIABLES, MEAN-SQUARE ERROR, CRITIQUE

Abstract

Gross [J. Gross, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57-64] proposed a restricted ridge regression estimator when exact restrictions are assumed to hold. When there are stochastic linear restrictions on the parameter vector, we introduce a new estimator by combining ideas underlying the mixed and the ridge regression estimators under the assumption that the errors are not independent and identically distributed. Apart from [J. Gross, Restricted ridge estimation, Statistics & Probability Letters 65 (2003) 57-64], we call this new estimator as the stochastic restricted ridge regression (SRRR) estimator. The performance of the SRRR estimator over the mixed estimator in respect of the variance and the mean square error matrices is examined. We also illustrate our findings with a numerical example. The shrinkage generalized least squares (GLS) and the stochastic restricted shrinkage GLS estimators are proposed. (C) 2009 Elsevier Inc. All rights reserved.