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Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility

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

No 22615, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: E17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ets, nep-for, nep-mac, nep-ore and nep-sog
Date: 2016-09
Note: AP EFG IFM
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Citations: View citations in EconPapers (4) Track citations by RSS feed

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) Downloads
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 (2015) Downloads
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