Variable Selection in Seemingly Unrelated Regressions with Random Predictors

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

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Year of publication:	2018
Authors:	Puelz, David
Other Persons:	Hahn, P. Richard (contributor) ; Carvalho, Carlos M. (contributor)
Publisher:	[2018]: [S.l.] : SSRN
Subject:	Regressionsanalyse \| Regression analysis \| Schätztheorie \| Estimation theory \| Prognoseverfahren \| Forecasting model

Extent:	1 Online-Ressource (21 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	In: Bayesian Analysis, Volume 12, Number 4 (2017), 969-989. 'https://projecteuclid.org/euclid.ba/1488855633' https://projecteuclid.org/euclid.ba/1488855633 Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments 2017 erstellt
Other identifiers:	10.2139/ssrn.2785870 [DOI]
Classification:	C11 - Bayesian Analysis ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10012936084