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Research Working Paper |
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Real-Time Density Forecasts from VARs with Stochastic Volatility By Todd E. Clark Abstract
Central banks and other forecasters have become increasingly interested
in various aspects of density forecasts. However, recent sharp changes
in macroeconomic volatility such as the Great Moderation and the more
recent sharp rise in volatility associated with greater variation in
energy prices and the deep global recession pose significant
challenges to density forecasting. Accordingly, this paper examines,
with real-time data, density forecasts of U.S. GDP growth, unemployment,
inflation, and the federal funds rate from VAR models with stochastic
volatility. The model of interest extends the steady state prior BVAR of
Villani (2009) to include stochastic volatility, because, as found in
some prior work and this paper, incorporating informative priors on the
steady states of the model variables often improves the accuracy of
point forecasts. The evidence presented in the paper shows that adding
stochastic volatility to the BVAR with a steady state prior materially
improves the real-time accuracy of point and density forecasts. |