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

  • Sergey Ivashchenko
  • Semih Emre Çekin
  • Kevin KotzéEmail author
  • Rangan Gupta
Article
  • 20 Downloads

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 

JEL Classifications

C13 C32 E37 

Notes

Acknowledgements

The authors would like to thank the anonymous referees for valuable comments. All remaining errors are ours.

Supplementary material

10614_2019_9941_MOESM1_ESM.pdf (1.9 mb)
Supplementary material 1 (pdf 1968 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Institute of Regional Economy ProblemsRussian Academy of SciencesSt. PetersburgRussia
  2. 2.National Research University Higher School of EconomicsSt. PetersburgRussia
  3. 3.The Faculty of EconomicsSaint-Petersburg State UniversitySt. PetersburgRussia
  4. 4.Financial Research Institute, Ministry of Finance, Russian FederationMoscowRussia
  5. 5.Department of EconomicsTurkish-German UniversityIstanbulTurkey
  6. 6.School of EconomicsUniversity of Cape TownRondeboschSouth Africa
  7. 7.Department of EconomicsUniversity of PretoriaPretoriaSouth Africa

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