BVAR models in the context of cointegration: A Monte Carlo experiment
Luis Alvarez and
Fernando Ballabriga ()
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Fernando Ballabriga: ESADE
No 9405, Working Papers from Banco de España, Working Papers Homepage
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.
Keywords: Bayesian vector autoregression; cointegration; Monte Carlo experiment (search for similar items in EconPapers)
JEL-codes: C11 C32 (search for similar items in EconPapers)
Pages: 41 pages
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:9405
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