Posterior inference on the degrees of freedom parameter in multivariate-t regression models

Siddharta Chib; Jacek Osiewalski; Mark F.J. Steel

doi:10.1016/0165-1765(91)90076-W

Posterior inference on the degrees of freedom parameter in multivariate-t regression models

Siddharta Chib
, Jacek Osiewalski
, Mark F.J. Steel

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

This paper considers the nonlinear regression model with errors that follow the multivariate Student-t distribution with ν degrees of freedom. We specify general conditions on the overall prior structure under which the prior of ν is not updated by the sample information, and provide an example in which learning about ν is not precluded.

Original language	English
Pages (from-to)	391-397
Number of pages	7
Journal	Economics Letters
Volume	37
Issue number	4
DOIs	https://doi.org/10.1016/0165-1765(91)90076-W
State	Published - Dec 1991

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title = "Posterior inference on the degrees of freedom parameter in multivariate-t regression models",

abstract = "This paper considers the nonlinear regression model with errors that follow the multivariate Student-t distribution with ν degrees of freedom. We specify general conditions on the overall prior structure under which the prior of ν is not updated by the sample information, and provide an example in which learning about ν is not precluded.",

author = "Siddharta Chib and Jacek Osiewalski and Steel, \{Mark F.J.\}",

year = "1991",

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AU - Chib, Siddharta

AU - Osiewalski, Jacek

AU - Steel, Mark F.J.

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Y1 - 1991/12

N2 - This paper considers the nonlinear regression model with errors that follow the multivariate Student-t distribution with ν degrees of freedom. We specify general conditions on the overall prior structure under which the prior of ν is not updated by the sample information, and provide an example in which learning about ν is not precluded.

AB - This paper considers the nonlinear regression model with errors that follow the multivariate Student-t distribution with ν degrees of freedom. We specify general conditions on the overall prior structure under which the prior of ν is not updated by the sample information, and provide an example in which learning about ν is not precluded.

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U2 - 10.1016/0165-1765(91)90076-W

DO - 10.1016/0165-1765(91)90076-W

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SP - 391

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JO - Economics Letters

JF - Economics Letters

IS - 4

ER -

Posterior inference on the degrees of freedom parameter in multivariate-t regression models

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