2026-08-17T17:48:15Zhttps://repositorio.bde.es/oai/requestoai:repositorio.bde.es:123456789/65302025-03-04T08:43:33Zcom_123456789_5661com_123456789_21col_123456789_5703
Repositorio Institucional
author
Álvarez, Luis J.
author
Ballabriga Claveria, Fernando
2019-08-10T17:21:33Z
2019-08-10T17:21:33Z
1994-02-28
ISBN: 8477932883
https://repositorio.bde.es/handle/123456789/6530
000086694
DTRA-199405-eng
The kind of prior typically employed in Bayesian vector autoregression (BVAR) analysis has aroused widespread suspicion about the ability of these models to capture long-run patterns. This paper specifies a bivariate cointegrated stochastic process and conducts a Monte|Carlo experiment to assess the small sample performance of two classical and two Bayesian estimation methods commonly applied to VAR models. In addition, a proposal to introduce a new dimension to the prior information in order to allow for explicit account of long-run restrictions is suggested and evaluated in the light of the experiment. The results of the experiment show that: the Minnesota -type prior with hyperparameter search performs well, suggesting that the prevalent suspicion about the inability of this prior to capture long-run patterns is not well-grounded
en
Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
BVAR models in the context of cointegration : a Monte Carlo experiment
Documento de trabajo
URL
https://repositorio.bde.es/bitstream/123456789/6530/1/dt9405e.pdf
File
MD5
f01aff17262340b23dbad76dee265f8b
2220716
application/pdf
dt9405e.pdf
URL
https://repositorio.bde.es/bitstream/123456789/6530/2/dt9405e.pdf.txt
File
MD5
9d33ea71ea841c42581e14961c9f8a08
57203
text/plain
dt9405e.pdf.txt