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Incorporating Judgments and Dealing with Data Uncertainty in Forecasting at the Czech National Bank

Author

Listed:
  • Jan Bruha
  • Tibor Hledik
  • Tomas Holub
  • Jiri Polansky
  • Jaromir Tonner

Abstract

This paper focuses on the forecasting process at the Czech National Bank with an emphasis on incorporating expert judgments into forecasts and addressing data uncertainty. At the beginning, the core model and the forecasting process are described and it is presented how data and the underlying uncertainty are handled. The core of the paper contains five case studies, which reflect policy issues addressed during forecasting rounds since 2008. Each case study first describes a particular forecasting problem, then the way how the issue was addressed, and finally the effect of incorporating off-model information into the forecast is briefly summarized. The case studies demonstrate that a careful incorporation of expert information into a structural framework may be useful for generating economically intuitive forecasts even during very turbulent times, and we show that such judgements may have important monetary policy implications.

Suggested Citation

  • Jan Bruha & Tibor Hledik & Tomas Holub & Jiri Polansky & Jaromir Tonner, 2013. "Incorporating Judgments and Dealing with Data Uncertainty in Forecasting at the Czech National Bank," Research and Policy Notes 2013/02, Czech National Bank, Research Department.
  • Handle: RePEc:cnb:rpnrpn:2013/02
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    File URL: http://www.cnb.cz/en/research/research_publications/irpn/download/rpn_2_2013.pdf
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    References listed on IDEAS

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    2. McCarthy, Patrick S, 1996. "Market Price and Income Elasticities of New Vehicles Demand," The Review of Economics and Statistics, MIT Press, vol. 78(3), pages 543-547, August.
    3. Jaromír Beneš & Andrew Binning & Kirdan Lees, 2008. "Incorporating judgement with DSGE models," Reserve Bank of New Zealand Discussion Paper Series DP2008/10, Reserve Bank of New Zealand.
    4. Richard H. Clarida & Diane Coyle, 1984. "Conditional Projection by Means of Kalman Filtering," Cowles Foundation Discussion Papers 702, Cowles Foundation for Research in Economics, Yale University.
    5. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2009. "New Keynesian Models: Not Yet Useful for Policy Analysis," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(1), pages 242-266, January.
    6. Jerome Adda & Russell Cooper, 2000. "Balladurette and Juppette: A Discrete Analysis of Scrapping Subsidies," Journal of Political Economy, University of Chicago Press, vol. 108(4), pages 778-806, August.
    7. Jaromir Tonner & Jiri Polansky & Osvald Vašíèek, 2011. "Parameter Drifting in a DSGE Model Estimated on Czech Data," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 510-524, November.
    8. Daniel F. Waggoner & Tao Zha, 1999. "Conditional Forecasts In Dynamic Multivariate Models," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 639-651, November.
    9. 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.
    10. Kevin Clinton & Marianne Johnson & Jaromir Benes & Douglas Laxton & Troy D Matheson, 2010. "Structural Models in Real Time," IMF Working Papers 10/56, International Monetary Fund.
    11. Jerome Adda & Russell Cooper, 2000. "The Dynamics of Car Sales: A Discrete Choice Approach," NBER Working Papers 7785, National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. repec:spr:jbuscr:v:12:y:2016:i:2:d:10.1007_s41549-016-0008-z is not listed on IDEAS
    2. Frantisek Brazdik & Zuzana Humplova & Frantisek Kopriva, 2014. "Evaluating a Structural Model Forecast: Decomposition Approach," Research and Policy Notes 2014/02, Czech National Bank, Research Department.
    3. Mirko Djukic & Tibor Hledik & Jiri Polansky & Ljubica Trajcev & Jan Vlcek, 2017. "A DSGE Model with Financial Dollarization - the Case of Serbia," Working Papers 2017/02, Czech National Bank, Research Department.
    4. Frantisek Brazdik & Michal Franta, 2017. "A BVAR Model for Forecasting of Czech Inflation," Working Papers 2017/7, Czech National Bank, Research Department.
    5. Jan Bruha & Jiri Polansky, 2014. "The Housing Sector over Business Cycles: Empirical Analysis and DSGE Modelling," Working Papers 2014/12, Czech National Bank, Research Department.
    6. Jan Bruha & Jiri Polansky, 2015. "Empirical Analysis of Labor Markets over Business Cycles: An International Comparison," Working Papers 2015/15, Czech National Bank, Research Department.
    7. Jan Bruha & Jaromir Tonner, 2017. "An Exchange Rate Floor as an Instrument of Monetary Policy: An Ex-post Assessment of the Czech Experience," Working Papers 2017/04, Czech National Bank, Research Department.
    8. Jan Babecky & Kamil Galuscak & Diana Zigraiova, 2015. "Labour Market Adjustment since the Global Financial Crisis: Evidence from a Survey of Czech Firms," Research and Policy Notes 2015/01, Czech National Bank, Research Department.
    9. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    10. Michal Franta & Tomas Holub & Petr Kral & Ivana Kubicova & Katerina Smidkova & Borek Vasicek, 2014. "The Exchange Rate as an Instrument at Zero Interest Rates: The Case of the Czech Republic," Research and Policy Notes 2014/03, Czech National Bank, Research Department.

    More about this item

    Keywords

    DSGE models; forecasting; Kalman filter; monetary policy.;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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