Hierarchical shrinkage priors for dynamic regressions with many predictors

This paper examines the properties of Bayes shrinkage estimators for dynamic regressions that are based on hierarchical versions of the typical normal prior. Various popular penalized least squares estimators for shrinkage and selection in regression models can be recovered using a single hierarchic...

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Bibliographic details
Volume:	29
Main Author:	Korobilis, Dimitris
Format:	Journal Article
Language:	English
Zielgruppe:	Trade Academic
Place of publication:	AMSTERDAM Elsevier B.V 01.01.2013 ELSEVIER SCIENCE BV Elsevier Sequoia S.A
published in:	International journal of forecasting Vol. 29; no. 1; pp. 43 - 59
ORCID:	0000-0001-9146-3008
Data of publication:	January-March 2013
ISSN:	0169-2070 1872-8200
EISSN:	1872-8200
Discipline:	Social Sciences (General) Economics
Subjects:	Factor model Shrinkage Forecasting Bayesian lasso Variable selection Economics Business & Economics Social Sciences Management Studies Economic models Economic forecasting Regression analysis Macroeconomics Bayesian analysis United States--US Factor model Shrinkage Forecasting Bayesian lasso Variable selection PRIOR DISTRIBUTIONS INFLATION MODEL GIBBS SELECTION
Online Access:	available in Bonn?
CODEN:	IJFOEK
Database:	Social Sciences Citation Index Web of Knowledge Web of Science - Social Sciences Citation Index - 2013 Web of Science CrossRef Academic OneFile (A&I only) Database information Databases - DBIS