Independence Properties of the Truncated Multivariate Elliptical Distributions

Michael Levine; Donald Richards; Jianxi Su

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arXiv · 1904.06412

Independence Properties of the Truncated Multivariate Elliptical Distributions

Michael Levine·Donald Richards·Jianxi Su

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Abstract

Truncated multivariate distributions arise extensively in econometric modelling when non-negative random variables are intrinsic to the data-generation process. More broadly, truncated multivariate distributions have appeared in censored and truncated regression models, simultaneous equations modelling, multivariate regression, and applications going back to the now-classic papers of Amemiya (1974) and Heckman (1976). In some applications of truncated multivariate distributions, there arises the problem of characterizing the distribution through correlation and independence properties of sub-vectors. In this paper, we characterize the truncated multivariate normal random vectors for which two complementary sub-vectors are mutually independent. Further, we characterize the multivariate truncated elliptical distributions, proving that if two complementary sub-vectors are mutually independent then the distribution of the joint vector is truncated multivariate normal, as is the distribution of each sub-vector. As an application, we apply the independence criterion to test the hypothesis of independence of the entrance examination scores and subsequent course averages achieved by a sample of university students; to do so, we verify the regularity conditions underpinning a classical theorem of Wilks on the asymptotic null distribution of the likelihood ratio test statistic.

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Michael Levine, Donald Richards, Jianxi Su. 2019-04-12. Independence Properties of the Truncated Multivariate Elliptical Distributions. https://arxiv.org/abs/1904.06412

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