Abstract
Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome over-parameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet-Laplace global-local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.
Original language | English |
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Article number | 109120 |
Journal | Economics Letters |
Volume | 191 |
Early online date | 30 Mar 2020 |
DOIs | |
Publication status | Published - Jun 2020 |
Keywords
- mixed frequency
- variational inference (VI)
- vector autoregression
- stochastic volatility
- hierarchical prior
- forecasting