Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility
Francis Diebold (),
Frank Schorfheide () and
No 22615, NBER Working Papers from National Bureau of Economic Research, Inc
Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating stochastic volatility improves DSGE forecasts (point, interval, and density). We examine real-time forecast accuracy for key macroeconomic variables including output growth, inflation, and the policy rate. We find that incorporating stochastic volatility in DSGE models of macroeconomic fundamentals markedly improves their density forecasts, just as incorporating stochastic volatility in models of financial asset returns improves their density forecasts.
JEL-codes: E17 (search for similar items in EconPapers)
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Published as Francis X. Diebold & Frank Schorfheide & Minchul Shin, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, vol 201(2), pages 322-332.
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Journal Article: Real-time forecast evaluation of DSGE models with stochastic volatility (2017)
Working Paper: Real-time forecast evaluation of DSGE models with stochastic volatility (2017)
Working Paper: Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility (2015)
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