Real-time forecast evaluation of DSGE models with stochastic volatility

Francis Diebold, Frank Schorfheide and Minchul Shin

No 577, CFS Working Paper Series from Center for Financial Studies (CFS)

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
New Economics Papers: this item is included in nep-dge, nep-ets, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (47)

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Related works:
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 (2016)
Working Paper: Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:577

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