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Forecasting with Second-Order Approximations and Markov Switching DSGE Models

Sergey Ivashchenko, Semih Çekin, Kevin Kotzé and Rangan Gupta ()
Additional contact information
Semih Çekin: Department of Economics, Turkish-German University, Istanbul, Turkey
Kevin Kotzé: School of Economics, University of Cape Town, Rondebosch, South Africa.

No 201862, Working Papers from University of Pretoria, Department of Economics

Abstract: This paper compares 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. These results are compared to those of a model that does not incorporate any regime-switching. 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).

Keywords: Regime-switching; second-order approximation; non-linear MS-DSGE estimation; forecasting (search for similar items in EconPapers)
JEL-codes: C13 C32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-for, nep-mac and nep-ore
Date: 2018-09
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