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Forecasting with Second-Order Approximations and Markov Switching DSGE Models

Author

Listed:
  • Sergey Ivashchenko

    () (The Institute of Regional Economy Problems (Russian Academy of Sciences), St. Petersburg, Russia; National Research University Higher School of Economics, St. Petersburg, Russia; The Faculty of Economics of Saint-Petersburg State University, St. Petersburg, Russia and Financial Research Institute, Ministry of Finance, Russian Federation, Moscow, Russia.)

  • Semih Emre Çekin

    (Department of Economics, Turkish-German University, Istanbul, Turkey)

  • Kevin Kotzé

    (School of Economics, University of Cape Town, Rondebosch, South Africa.)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

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).

Suggested Citation

  • Sergey Ivashchenko & Semih Emre Çekin & Kevin Kotzé & Rangan Gupta, 2018. "Forecasting with Second-Order Approximations and Markov Switching DSGE Models," Working Papers 201862, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201862
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    References listed on IDEAS

    as
    1. Sergey Ivashchenko, 2014. "DSGE Model Estimation on the Basis of Second-Order Approximation," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 71-82, January.
    2. repec:oup:oxford:v:34:y:2018:i:1-2:p:269-286. is not listed on IDEAS
    3. Andrew Foerster & Juan F. Rubio‐Ramírez & Daniel F. Waggoner & Tao Zha, 2016. "Perturbation methods for Markov‐switching dynamic stochastic general equilibrium models," Quantitative Economics, Econometric Society, vol. 7(2), pages 637-669, July.
    4. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    5. Zheng Liu & Daniel F. Waggoner & Tao Zha, 2011. "Sources of macroeconomic fluctuations: A regime‐switching DSGE approach," Quantitative Economics, Econometric Society, vol. 2(2), pages 251-301, July.
    6. Sergey Ivashchenko, 2016. "Estimation and filtering of nonlinear MS-DSGE models," HSE Working papers WP BRP 136/EC/2016, National Research University Higher School of Economics.
    7. Philip Liu & Haroon Mumtaz, 2011. "Evolving Macroeconomic Dynamics in a Small Open Economy: An Estimated Markov Switching DSGE Model for the UK," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(7), pages 1443-1474, October.
    8. Ricardo Reis, 2018. "Is something really wrong with macroeconomics?," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 132-155.
    9. Junior Maih, 2014. "Efficient Perturbation Methods for Solving Regime-Switching DSGE Models," Working Papers No 10/2014, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Olivier Blanchard, 2016. "Do DSGE Models Have a Future?," Policy Briefs PB16-11, Peterson Institute for International Economics.
    11. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    12. repec:wly:quante:v:9:y:2018:i:2:p:903-944 is not listed on IDEAS
    13. Clarke, Kevin A., 2007. "A Simple Distribution-Free Test for Nonnested Model Selection," Political Analysis, Cambridge University Press, vol. 15(03), pages 347-363, June.
    14. Pichler Paul, 2008. "Forecasting with DSGE Models: The Role of Nonlinearities," The B.E. Journal of Macroeconomics, De Gruyter, vol. 8(1), pages 1-35, July.
    15. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    16. Joseph E Stiglitz, 2018. "Where modern macroeconomics went wrong," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 70-106.
    17. Zheng Liu & Daniel Waggoner & Tao Zha, 2009. "Asymmetric Expectation Effects of Regime Shifts in Monetary Policy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 12(2), pages 284-303, April.
    18. Jesús Fernández‐Villaverde & Oren Levintal, 2018. "Solution methods for models with rare disasters," Quantitative Economics, Econometric Society, vol. 9(2), pages 903-944, July.
    19. Julio J. Rotemberg, 1982. "Monopolistic Price Adjustment and Aggregate Output," Review of Economic Studies, Oxford University Press, vol. 49(4), pages 517-531.
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    More about this item

    Keywords

    Regime-switching; second-order approximation; non-linear MS-DSGE estimation; forecasting;

    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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