TY - JOUR
T1 - Bayesian methods for dynamic multivariate models
AU - Sims, Christopher A.
AU - Zha, Tao
N1 - Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 1998/11
Y1 - 1998/11
N2 - If dynamic multivariate models are to be used to guide decision-making, it is important that probability assessments of forecasts or policy projections be provided. When identified Bayesian vector autoregression (VAR) models are presented with error bands in the existing literature, both conceptual and numerical problems have not been dealt with in an internally consistent way. In this paper we develop methods to introduce prior information in both reduced-form and structural VAR models without introducing substantial new computational burdens. Our approach makes it feasible to use a single, large dynamic framework (for example, 20-variable models) for tasks of policy projections.
AB - If dynamic multivariate models are to be used to guide decision-making, it is important that probability assessments of forecasts or policy projections be provided. When identified Bayesian vector autoregression (VAR) models are presented with error bands in the existing literature, both conceptual and numerical problems have not been dealt with in an internally consistent way. In this paper we develop methods to introduce prior information in both reduced-form and structural VAR models without introducing substantial new computational burdens. Our approach makes it feasible to use a single, large dynamic framework (for example, 20-variable models) for tasks of policy projections.
UR - http://www.scopus.com/inward/record.url?scp=0347466670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0347466670&partnerID=8YFLogxK
U2 - 10.2307/2527347
DO - 10.2307/2527347
M3 - Article
AN - SCOPUS:0347466670
VL - 39
SP - 949
EP - 968
JO - International Economic Review
JF - International Economic Review
SN - 0020-6598
IS - 4
ER -