Bayesian Analysis

Variable Selection in Seemingly Unrelated Regressions with Random Predictors

David Puelz, P. Richard Hahn, and Carlos M. Carvalho

Full-text: Open access

Abstract

This paper considers linear model selection when the response is vector-valued and the predictors, either all or some, 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.

Article information

Source
Bayesian Anal. Volume 12, Number 4 (2017), 969-989.

Dates
First available in Project Euclid: 7 March 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1488855633

Digital Object Identifier
doi:10.1214/17-BA1053

Keywords
decoupling shrinkage and selection seemingly unrelated regressions penalized utility selection

Rights
Creative Commons Attribution 4.0 International License.

Citation

Puelz, David; Hahn, P. Richard; Carvalho, Carlos M. Variable Selection in Seemingly Unrelated Regressions with Random Predictors. Bayesian Anal. 12 (2017), no. 4, 969--989. doi:10.1214/17-BA1053. https://projecteuclid.org/euclid.ba/1488855633


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