IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v56y2020i4d10.1007_s10614-019-09941-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-019-09941-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ricardo Reis, 2018. "Is something really wrong with macroeconomics?," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 132-155.
    2. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
    3. 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.
    4. 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.
    5. Jesper Lindé, 2018. "DSGE models: still useful in policy analysis?," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 269-286.
    6. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, September.
    7. 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.
    8. Jesús Fernández‐Villaverde & Oren Levintal, 2018. "Solution methods for models with rare disasters," Quantitative Economics, Econometric Society, vol. 9(2), pages 903-944, July.
    9. 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.
    10. 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.
    11. Clarke, Kevin A., 2007. "A Simple Distribution-Free Test for Nonnested Model Selection," Political Analysis, Cambridge University Press, vol. 15(3), pages 347-363, July.
    12. 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.
    13. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-31.
    14. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    15. 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.
    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. 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.
    19. 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.
    20. Peter N. Ireland, 2011. "A New Keynesian Perspective on the Great Recession," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 31-54, February.
    21. Balcilar, Mehmet & Gupta, Rangan & Kotzé, Kevin, 2015. "Forecasting macroeconomic data for an emerging market with a nonlinear DSGE model," Economic Modelling, Elsevier, vol. 44(C), pages 215-228.
    22. Olivier Blanchard, 2016. "Do DSGE Models Have a Future?," Policy Briefs PB16-11, Peterson Institute for International Economics.
    23. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2017. "Lower Bounds on Approximation Errors to Numerical Solutions of Dynamic Economic Models," Econometrica, Econometric Society, vol. 85, pages 991-1012, May.
    24. Julio J. Rotemberg, 1982. "Monopolistic Price Adjustment and Aggregate Output," Review of Economic Studies, Oxford University Press, vol. 49(4), pages 517-531.
    25. 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.
    Full references (including those not matched with items on IDEAS)

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:56:y:2020:i:4:d:10.1007_s10614-019-09941-8. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sonal Shukla) or (Springer Nature Abstracting and Indexing). General contact details of provider: http://www.springer.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.