Quotation Huber, Florian. 2016. Density Forecasting using Bayesian Global Vector Autoregressions with Stochastic Volatility. International Journal of Forecasting 32 (3): S. 818-837.


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Abstract

This paper develops a Bayesian global vector autoregressive model with stochastic volatility. Three variants of stochastic volatility are implemented to improve the existing homoscedastic framework. In our baseline model, we assume that the variance-covariance matrix is driven by a set of idiosyncratic, country-specific and regional factors. By 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 stochastic volatility influences predictive accuracy along three dimensions. First, it helps to improve the overall predictive fit of our model. Second, it helps to make the model more resilient with respect to outliers and economic crises. Finally, taking a regional stance reveals that forecasts in developing economies tend to profit more from stochastic volatility.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal International Journal of Forecasting
Citation Index SSCI
WU-Journal-Rating new STRAT-C, VW-D, WH-B
Language English
Title Density Forecasting using Bayesian Global Vector Autoregressions with Stochastic Volatility
Volume 32
Number 3
Year 2016
Page from 818
Page to 837
Reviewed? Y
DOI 10.1016/j.ijforecast.2015.12.008

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People
Huber, Florian (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Institute for Macroeconomics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
5323 Econometrics (Details)
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