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

Serie: Documentos de Trabajo. 9405.

Autor: Luis J. Álvarez and Fernando C. Ballabriga

Métodos cuantitativos

• Tipos de cambio
• Incertidumbre
• Situación económica

Documento completo

BVAR models in the context of cointegration: a Monte Carlo experiment (2 MB)

Resumen

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: (i) the Minnesota-type prior with hyperparameter search performs well, suggesting that the prevalent suspicion about the inability of this prior to capture longrun patterns is not well-grounded; (ii) the fine-tuning of the prior is crucial; and (iii) adding long-run restrictions to the prior does not provide improvements in the case analyzed.