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BVAR models in the context of cointegration : a Monte Carlo experiment

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Issue Date
1994
Physical description
41 p.
ISBN
8477932883
Abstract
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

the fine-tunning of the prior is crucial

and adding long-run restrictions to the prior does not provide improvements in the case analyzed.(jag)(fbg)(jha)
Publish on
Documentos de trabajo / Banco de España, 9405
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