Variable Selection in Seemingly Unrelated Regressions with Random Predictors

Bayesian Analysis, Volume 12, Number 4 (2017), 969-989. https://projecteuclid.org/euclid.ba/1488855633

21 Pages Posted: 29 May 2016 Last revised: 15 Oct 2018

See all articles by David Puelz

David Puelz

University of Chicago - Booth School of Business

P. Richard Hahn

Arizona State University (ASU) - School of Mathematical and Statistical Sciences

Carlos M. Carvalho

University of Texas at Austin - Red McCombs School of Business

Date Written: 2017

Abstract

This paper considers linear model selection when the response is vector-valued and the predictors are randomly observed. We propose a new approach that decouples statistical inference from the selection step in a "post-inference model summarization" strategy. We study the impact of predictor uncertainty on the model selection procedure. The method is demonstrated through an application to asset pricing.

JEL Classification: C11, C61

Suggested Citation

Puelz, David and Hahn, P. Richard and Carvalho, Carlos M., Variable Selection in Seemingly Unrelated Regressions with Random Predictors (2017). Bayesian Analysis, Volume 12, Number 4 (2017), 969-989. https://projecteuclid.org/euclid.ba/1488855633 , Available at SSRN: https://ssrn.com/abstract=2785870 or http://dx.doi.org/10.2139/ssrn.2785870

David Puelz (Contact Author)

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States

P. Richard Hahn

Arizona State University (ASU) - School of Mathematical and Statistical Sciences ( email )

Tempe, AZ 85287-1804
United States

Carlos M. Carvalho

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

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