 Density forecasting using Bayesian global vector autoregressions with stochastic volatilityInternational Journal of Forecasting, 2016, vol. 32, issue 3, 818-837 Abstract: This paper develops a Bayesian global vector autoregressive model with stochastic volatility. Three variants of the stochastic volatility are implemented in an attempt to improve the existing homoscedastic framework. Our baseline model assumes that the variance–covariance matrix is driven by a set of idiosyncratic, country-specific and regional factors. In contrast, the second specification adopted implies that the error variance of each equation is determined by an independent stochastic process. The final specification assumes that the country-specific volatility follows a single factor, which leads to significant computational gains. Considering a range of competing models, we forecast a large panel of macroeconomic variables and find that the stochastic volatility influences the predictive accuracy in three ways. First, it helps to improve the overall predictive fit of our model. Second, it helps to make the model more resilient to outliers and economic crises. Finally, taking a regional stance reveals that the forecasts in developing economies tend to profit more from the use of the stochastic volatility. Keywords: Density forecasting; Large panels; Factor stochastic volatility (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:818-837 DOI: 10.1016/j.ijforecast.2015.12.008 Access Statistics for this article International Journal of Forecasting is currently edited by R. J. Hyndman More articles in International Journal of Forecasting from Elsevier |
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