Yazar "Ivashchenko, Sergey" seçeneğine göre listele

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    Forecasting with second-order approximations and markov-switching DSGE models
    (Springer, 2020) Ivashchenko, Sergey; Çekin, Semih Emre; Kotze, Kevin; Gupta, Rangan; Çekin, Semih Emre
    This paper considers the out-of-sample forecasting performance of first- and second-order perturbation approximations for DSGE models that incorporate Markov-switching behaviour in the policy reaction function and the volatility of shocks. The results suggest that second-order approximations provide an improved forecasting performance in models that do not allow for regime-switching, while for the MS-DSGE models, a first-order approximation would appear to provide better out-of-sample properties. In addition, we find that over short-horizons, the MS-DSGE models provide superior forecasting results when compared to those models that do not allow for regime-switching (at both perturbation orders).
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    Öğe
    Real-time forecast of DSGE models with time-varying volatility in GARCH form
    (Elsevier Science Inc, 2024) Cekin, Semih Emre; Ivashchenko, Sergey; Gupta, Rangan; Lee, Chien-Chiang
    Recent research shows that time -varying volatility plays a crucial role in non-linear modeling. Contributing to this literature, we suggest an approach that allows for straightforward computation of DSGE models with timevarying volatility, where the volatility component is formulated as a GARCH process. As an application of our approach, we examine the forecasting performance of this DSGE-GARCH model using euro area 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 efficiency. In addition to these results, our approach reveals interesting properties and dynamics of time -varying correlations (conditional correlations).