Series: Working Papers. 9405.
Author: Luis J. Álvarez and Fernando C. Ballabriga.
Quantitative methods
- Exchange rates
- Uncertainty
- Economic situation
Full document
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: (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.