Samorodnitsky, Gennady Rachev, Svetlozar T. Kurz-Kim, Jeong-Ryeol
Year of Publication:
Discussion paper Series 1 / Volkswirtschaftliches Forschungszentrum der Deutschen Bundesbank 2005,21
Under the symmetric á-stable distributional assumption for the disturbances, Blattberg et al (1971) consider unbiased linear estimators for a regression model with non-stochastic regressors. We consider both the rate of convergence to the true value and the asymptotic distribution of the normalized error of the linear unbiased estimators. By doing this, we allow the regressors to be stochastic and disturbances to be heavy-tailed with either finite or infinite variances, where the tail-thickness parameters of the regressors and disturbances may be different.
Asymptotic distribution rate of convergence stochastic regressor stable non-Gaussian finite or infinite variance heavy tails