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

Author

Listed:
  • Sergey Ivashchenko

    (Russian Academy of Sciences
    National Research University Higher School of Economics
    Saint-Petersburg State University
    Financial Research Institute, Ministry of Finance, Russian Federation)

  • Semih Emre Çekin

    (Turkish-German University)

  • Kevin Kotzé

    (University of Cape Town)

  • Rangan Gupta

    (University of Pretoria)

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).

Suggested Citation

  • Sergey Ivashchenko & Semih Emre Çekin & Kevin Kotzé & Rangan Gupta, 2020. "Forecasting with Second-Order Approximations and Markov-Switching DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 747-771, December.
    • Handle: RePEc:kap:compec:v:56:y:2020:i:4:d:10.1007_s10614-019-09941-8
    DOI: 10.1007/s10614-019-09941-8
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    Cited by:

    1. Sergey Ivashchenko & Semih Emre Cekin & Rangan Gupta, 2022. "Real-Time Forecast of DSGE Models with Time-Varying Volatility in GARCH Form," Working Papers 202204, University of Pretoria, Department of Economics.

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    More about this item

    Keywords

    Regime-switching; Second-order approximation; Non-linear MS-DSGE estimation; Forecasting;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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