Variable selection in seemingly unrelated regressions with random predictors

David Puelz, Paul Hahn, Carlos M. Carvalho

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper considers linear model selection when the response is vectorvalued 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.

Original languageEnglish (US)
Pages (from-to)969-989
Number of pages21
JournalBayesian Analysis
Volume12
Issue number4
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

Fingerprint

Seemingly Unrelated Regression
Variable Selection
Model Selection
Predictors
Asset Pricing
Summarization
Selection Procedures
Statistical Inference
Linear Model
Uncertainty
Costs
Model
Strategy

Keywords

  • Decoupling shrinkage and selection
  • Penalized utility selection
  • Seemingly unrelated regressions

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

Cite this

Variable selection in seemingly unrelated regressions with random predictors. / Puelz, David; Hahn, Paul; Carvalho, Carlos M.

In: Bayesian Analysis, Vol. 12, No. 4, 01.01.2017, p. 969-989.

Research output: Contribution to journalArticle

Puelz, David ; Hahn, Paul ; Carvalho, Carlos M. / Variable selection in seemingly unrelated regressions with random predictors. In: Bayesian Analysis. 2017 ; Vol. 12, No. 4. pp. 969-989.
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