IDEAS home Printed from https://ideas.repec.org/a/asi/ajemod/2020p30-54.html
   My bibliography  Save this article

An Estimated Bayesian DSGE Model for Kazakhstan

Author

Listed:
  • Nurdaulet Abilov

    (NAC Analytica, Nazarbayev University, Nur-Sultan, Kazakhstan.)

Abstract

A small scale open economy model is estimated for Kazakhstan via Bayesian methods. The model explicitly takes into account the dependence of the economy on commodity exports and also accounts for risk premium shocks in the foreign exchange market. The main contribution of the research is that it is the first DSGE model in literature estimated via Bayesian methods for Kazakhstan. The results of the model are used to determine the historical contribution of structural shocks to endogenous variables, forecast error variance decomposition of observed macroeconomic variables and impulse responses of important endogenous variables to various shocks. It has been found that the output gap turned significantly negative during the Great Recession and the negative oil price shock. The effect of contractionary monetary policy is found to be negative on output gap, but it negligibly affects the inflation rate in the economy. Risk premium shocks are found to account for almost 60% of forecast error variance decomposition of nominal exchange rate of tenge over all horizons.

Suggested Citation

  • Nurdaulet Abilov, 2020. "An Estimated Bayesian DSGE Model for Kazakhstan," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 8(1), pages 30-54, March.
  • Handle: RePEc:asi:ajemod:2020:p:30-54
    as

    Download full text from publisher

    File URL: http://www.aessweb.com/download.php?id=4872
    Download Restriction: no

    File URL: http://www.aessweb.com/journals/5009/March2020
    Download Restriction: no

    References listed on IDEAS

    as
    1. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.),Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393, Elsevier.
    2. Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.
    3. Vasco Cúrdia & Michael Woodford, 2010. "Credit Spreads and Monetary Policy," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(s1), pages 3-35, September.
    4. Jordi Galí & Tommaso Monacelli, 2005. "Monetary Policy and Exchange Rate Volatility in a Small Open Economy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 707-734.
    5. Kristoffer P. Nimark, 2009. "A Structural Model of Australia as a Small Open Economy," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 42(1), pages 24-41, March.
    6. Abel, Andrew B, 1990. "Asset Prices under Habit Formation and Catching Up with the Joneses," American Economic Review, American Economic Association, vol. 80(2), pages 38-42, May.
    7. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    8. Aliya Algozhina, 2016. "Monetary Policy Rule, Exchange Rate Regime, and Fiscal Policy Cyclicality in a Developing Oil Economy," CERGE-EI Working Papers wp572, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Olivier Blanchard, 2018. "On the future of macroeconomic models," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 43-54.
    10. Ravn, Morten O. & Sterk, Vincent, 2016. "Macroeconomic fluctuations with HANK & SAM: an analytical approach," LSE Research Online Documents on Economics 86177, London School of Economics and Political Science, LSE Library.
    11. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    12. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    13. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    14. Luk, Sheung Kan & Vines, David, 2011. "Financial-Friction Macroeconomics with Highly Leveraged Financial Institutions," CEPR Discussion Papers 8576, C.E.P.R. Discussion Papers.
    15. Klaus Weyerstrass & Daniela Grozea-Helmenstein, 2013. "A Macroeconometric Model for Serbia," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 19(2), pages 85-106, May.
    16. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    17. Joseph E Stiglitz, 2018. "Where modern macroeconomics went wrong," Oxford Review of Economic Policy, Oxford University Press, vol. 34(1-2), pages 70-106.
    18. Barbara Rudolf & Mathias Zurlinden, 2014. "A compact open economy DSGE model for Switzerland," Economic Studies 2014-08, Swiss National Bank.
    19. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    20. Nurdaulet Abilov & Alisher Tolepbergen & Klaus Weyerstrass, 2018. "A Macroeconometric Model for Kazakhstan," NAC Analytica Working Paper 1, NAC Analytica, Nazarbayev University, revised Jul 2019.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    DSGE; Bayesian analysis; Small open economy; Historical decomposition; Impulse response analysis; Variance decomposition.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • 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:asi:ajemod:2020:p:30-54. 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: (Chan Hoi Yan). General contact details of provider: http://www.aessweb.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.