Bayesian compressed vector autoregressions

Macroeconomists are increasingly working with large Vector Autoregressions (VARs) where the number of parameters vastly exceeds the number of observations. Existing approaches either involve prior shrinkage or the use of factor methods. In this paper, we develop an alternative based on ideas from th...

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Bibliographic details
Volume:	210
Main Author:	Koop, Gary Korobilis, Dimitris Pettenuzzo, Davide
Format:	Journal Article
Language:	English
Zielgruppe:	Academic
Place of publication:	Elsevier B.V 01.05.2019 Elsevier Science Publishers
published in:	Journal of econometrics Vol. 210; no. 1; pp. 135 - 154
Data of publication:	May 2019
ISSN:	0304-4076 1872-6895
EISSN:	1872-6895
Discipline:	Economics Statistics Mathematics
Subjects:	Forecasting Multivariate time series Random projection C53 C11 Multivariate time series Random projection C32 Forecasting
Online Access:	available in Bonn?
Database:	CrossRef Gale General OneFile (A&I only) Academic OneFile (A&I only) Database information Databases - DBIS