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Real-time forecast evaluation of DSGE models with stochastic volatility

Francis Diebold (), Frank Schorfheide () and Minchul Shin

Journal of Econometrics, 2017, vol. 201, issue 2, 322-332

Abstract: 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.

Keywords: Dynamic stochastic general equilibrium model; Prediction; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: E17 E27 E37 E47 (search for similar items in EconPapers)
Date: 2017
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Related works:
Working Paper: Real-time forecast evaluation of DSGE models with stochastic volatility (2017) Downloads
Working Paper: Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility (2016) Downloads
Working Paper: Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility (2015) Downloads
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