Adaptive hierarchical priors for high-dimensional vector autoregressions

This paper proposes a simulation-free estimation algorithm for vector autoregressions (VARs) that allows fast approximate calculation of marginal parameter posterior distributions. We apply the algorithm to derive analytical expressions for independent VAR priors that admit a hierarchical representa...

Full description

Saved in:

Bibliographic details
Volume:	212
Main Author:	Korobilis, Dimitris Pettenuzzo, Davide
Format:	Journal Article
Language:	English
Zielgruppe:	Academic
Place of publication:	LAUSANNE Elsevier B.V 01.09.2019 ELSEVIER SCIENCE SA Elsevier Science Publishers
published in:	Journal of econometrics Vol. 212; no. 1; pp. 241 - 271
ORCID:	0000-0001-9146-3008
Data of publication:	September 2019
ISSN:	0304-4076 1872-6895
EISSN:	1872-6895
Discipline:	Economics Statistics Mathematics
Subjects:	Bayesian VARs Macroeconomic forecasting Large datasets Mixture prior Economics Mathematics, Interdisciplinary Applications Physical Sciences Social Sciences Social Sciences, Mathematical Methods Business & Economics Mathematics Mathematical Methods In Social Sciences Science & Technology Business schools Algorithms Business cycles Analysis Models Econometric models Central banks C53 Bayesian VARs C11 C32 C13 Macroeconomic forecasting Large datasets Mixture prior SHRINKAGE NEWS INFERENCE SELECTION REGRESSIONS
Online Access:	available in Bonn?
Database:	Social Sciences Citation Index Web of Science - Science Citation Index Expanded - 2019 Web of Knowledge Science Citation Index Expanded Web of Science - Social Sciences Citation Index – 2019 Web of Science CrossRef Gale General OneFile (A&I only) Academic OneFile (A&I only) Database information Databases - DBIS