Real-Time Forecast of DSGE Models with Time-Varying Volatility in GARCH Form

Sergey Ivashchenko, Semih Cekin, Rangan Gupta and Chien-Chiang Lee ()
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Sergey Ivashchenko: The North-Western Main Branch of the Bank of Russia; The Institute of Regional Economy Studies (Russian Academy of Sciences); The Financial Research Institute
Semih Cekin: Department of Economics, Turkish-German University, Istanbul, Turkey

No 202204, Working Papers from University of Pretoria, Department of Economics

Abstract: Recent research shows that time-varying volatility plays a crucial role in nonlinear modeling. Contributing to this literature, we suggest a DSGE-GARCH approach that allows for straight-forward computation of DSGE models with time-varying volatility. As an application of our approach, we examine the forecasting performance of the DSGE-GARCH model using Eurozone real-time data. Our findings suggest that the DSGE-GARCH approach is superior in out-of-sample forecasting performance in comparison to various other benchmarks for the forecast of inflation rates, output growth and interest rates, especially in the short term. Comparing our approach to the widely used stochastic volatility specification using in-sample forecasts, we also show that the DSGE-GARCH is superior in in-sample forecast quality and computational effciency. In addition to these results, our approach reveals interesting properties and dynamics of time-varying correlations (conditional correlations).

Keywords: DSGE; forecasting; GARCH; stochastic volatility; conditional correlations (search for similar items in EconPapers)
JEL-codes: C32 E30 E37 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2022-01
New Economics Papers: this item is included in nep-dge, nep-ecm, nep-for, nep-mac, nep-ore and nep-rmg
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Journal Article: Real-time forecast of DSGE models with time-varying volatility in GARCH form (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:202204

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