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This paper introduces shrinkage estimators for the parameter vector of a linear regression model with conditionally heteroscedastic errors such as the class of generalized autoregressive conditional heteroscedastic (GARCH) errors when some of the regression parameters are restricted to a subspace. We derive the asymptotic distributional biases and risks of the shrinkage estimators using a large sample theory. We show that if the shrinkage dimension exceeds two, the relative efficiency of thedoi:10.2991/jsta.2016.15.4.8 fatcat:lujf3r2iffftnpi6rr6aqlqtri