Title: On the distribution of the inverted linear compound of dependent F-variates and its application to the combination of forecasts
Authors: Liang, Kuo-Yuan
Lee, Jack C.
Shao, Kurt S. H.
資訊管理與財務金融系 註:原資管所+財金所
National Chiao Tung University
Department of Information Management and Finance
Keywords: combining weights;critical values;error-variance minimizing criterion;inverted F-variates;Pearson Type I approximation
Issue Date: 1-Nov-2006
Abstract: This paper establishes a sampling theory for an inverted linear combination of two dependent F-variates. It is found that the random variable is approximately expressible in terms of a mixture of weighted beta distributions. Operational results, including rth-order raw moments and critical values of the density are subsequently obtained by using the Pearson Type I approximation technique. As a contribution to the probability theory, our findings extend Lee & Hu's (1996) recent investigation on the distribution of the linear compound of two independent F-variates. In terms of relevant applied works, our results refine Dickinson's (1973) inquiry on the distribution of the optimal combining weights estimates based on combining two independent rival forecasts, and provide a further advancement to the general case of combining three independent competing forecasts. Accordingly, our conclusions give a new perception of constructing the confidence intervals for the optimal combining weights estimates studied in the literature of the linear combination of forecasts.
URI: http://dx.doi.org/10.1080/02664760600744330
ISSN: 0266-4763
DOI: 10.1080/02664760600744330
Volume: 33
Issue: 9
Begin Page: 961
End Page: 973
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