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

• Autorzy: Samorodnitsky G. , Rachey S. T. , Kurz-Kim J.-R. , Stoyanov S.
• Języki publikacji: EN

Abstrakty

EN

Under the symmetric а-stable distributional assumption for the disturbances, Blattberg and Sargent [3] consider unbiased line- ar estimators for a regression model with non-stochastic regressors. We study 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.

Słowa kluczowe

EN

asymptotic distribution; rate of convergence; stochastic regressor; stable non-Gaussian; finite or infinite variance; heavy tails

• Wydawca: Wrocław University of Science and Technology
• Czasopismo: Probability and Mathematical Statistics
• Rocznik: 2007
• Tom: Vol. 27, Fasc. 2
• Strony: 275--302
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Samorodnitsky G.

• School of Operations Research and Industrial Engineering and Cornell University Ithaca, NY 14853, USA

autor

Rachey S. T.

• Svetlozar T. Rachev Institut für Statistik und Mathematische Wirtschaftstheorie, Universität Karlsruhe, Kollegium am Schloss, D-76128 Karlsruhe, Germany

autor

Kurz-Kim J.-R.

• Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, D-60431 Frankfurt am Main, Germany

autor

Stoyanov S.

• FinAnalytica Inc., 440-F Camino del Remedio Santa Barbara, CA 93110, USA

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