Asymptotic distribution of linear unbiased estimators in the presence of heavy-tailed stochastic regressors and residuals
AbstractUnder 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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Bibliographic InfoPaper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2005,21.
Date of creation: 2005
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Asymptotic distribution; rate of convergence; stochastic regressor; stable non-Gaussian; finite or infinite variance; heavy tails;
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- Hamerle, Alfred & Liebig, Thilo & Scheule, Harald, 2004. "Forecasting Credit Portfolio Risk," Discussion Paper Series 2: Banking and Financial Studies 2004,01, Deutsche Bundesbank, Research Centre.
- Fecht, Falko & Huang, Kevin & Martin, Antoine, 2005.
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Discussion Paper Series 1: Economic Studies
2005,03, Deutsche Bundesbank, Research Centre.
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- Falko Fecht & Kevin X.D. Huang & Antoine Martin, 2007. "Financial Intermediaries, Markets, and Growth," Vanderbilt University Department of Economics Working Papers 0714, Vanderbilt University Department of Economics.
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