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Asymptotic distribution of linear unbiased estimators in the presence of heavy-tailed stochastic regressors and residuals


  • Samorodnitsky, Gennady
  • Rachev, Svetlozar T.
  • Kurz-Kim, Jeong-Ryeol


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.

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  • Samorodnitsky, Gennady & Rachev, Svetlozar T. & Kurz-Kim, Jeong-Ryeol, 2005. "Asymptotic distribution of linear unbiased estimators in the presence of heavy-tailed stochastic regressors and residuals," Discussion Paper Series 1: Economic Studies 2005,21, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdp1:4215

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    References listed on IDEAS

    1. Falko Fecht & Kevin X. D. Huang & Antoine Martin, 2008. "Financial Intermediaries, Markets, and Growth," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(4), pages 701-720, June.
    2. Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank.
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    Asymptotic distribution; rate of convergence; stochastic regressor; stable non-Gaussian; finite or infinite variance; heavy tails;

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