Data-based priors for vector error correction models
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Date
2020
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Abstract
We propose two data-based priors for vector error correction models. Both
priors lead to highly automatic approaches which require only minimal user
input. An empirical investigation reveals that Bayesian vector error correction
(BVEC) models equipped with our proposed priors turn out to scale well to
higher dimensions and to forecast well. In addition, we find that exploiting
information in the level variables has the potential for improving long-term
forecasts. Thus, working with VARs in first differences may ignore valuable
information. A simulation study reveals that it is beneficial, in terms of estimation
accuracy, to use BVEC in the presence of cointegration. But if there is
no cointegration, the proposed priors provide a sufficient amount of shrinkage
so that the BVEC model has a similar estimation accuracy compared to the
Bayesian vector autoregressive (BVAR) estimated in first differences.
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Keywords
BVAR, hierarchical prior, forecasting, cointegration