Forecasting with Second-Order Approximations and Markov-Switching DSGE Models

Sergey Ivashchenko (), Semih Çekin, Kevin Kotze and Rangan Gupta
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Sergey Ivashchenko: Russian Academy of Sciences

Computational Economics, 2020, vol. 56, issue 4, No 4, 747-771

Abstract: Abstract 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).

Keywords: Regime-switching; Second-order approximation; Non-linear MS-DSGE estimation; Forecasting; C13; C32; E37 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Forecasting with second-order approximations and Markov-switching DSGE models (2018)
Working Paper: Forecasting with Second-Order Approximations and Markov Switching DSGE Models (2018)
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DOI: 10.1007/s10614-019-09941-8

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