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 |
|---|---|
| Journal | Economics Letters |
| Publication status | Accepted/In press - 25 Mar 2020 |
Keywords
- mixed frequency
- variational inference (VI)
- vector autoregression
- stochastic volatility
- hierarchical prior
- forecasting
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Cite this
Gefang, D., Koop, G., & Poon, A. (Accepted/In press). Computationally efficient inference in large Bayesian mixed frequency VARs. Economics Letters.