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Structural Models in Real Time

Author

Listed:
  • Kevin Clinton
  • Marianne Johnson
  • Jaromir Benes
  • Douglas Laxton
  • Troy D Matheson

Abstract

This paper outlines a simple approach for incorporating extraneous predictions into structural models. The method allows the forecaster to combine predictions derived from any source in a way that is consistent with the underlying structure of the model. The method is flexible enough that predictions can be up-weighted or down-weighted on a case-by-case basis. We illustrate the approach using a small quarterly structural and real-time data for the United States.

Suggested Citation

  • 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.
  • Handle: RePEc:imf:imfwpa:10/56
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    File URL: http://www.imf.org/external/pubs/cat/longres.aspx?sk=23686
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    References listed on IDEAS

    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Ricardo Mestre & Peter McAdam, 2011. "Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 303-324, April.
    3. Michel Juillard & Ondrej Kamenik & Michael Kumhof & Douglas Laxton, 2006. "Measures of Potential Output from an Estimated DSGE Model of the United States," Working Papers 2006/11, Czech National Bank, Research Department.
    4. Eric Leeper, 2003. "An "Inflation Reports" Report," NBER Working Papers 10089, National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. Ali Alichi & Jaromir Benes & Joshua Felman & Irene Feng & Charles Freedman & Douglas Laxton & Evan C Tanner & David Vavra & Hou Wang, 2015. "Frontiers of Monetary Policymaking; Adding the Exchange Rate as a Tool to Combat Deflationary Risks in the Czech Republic," IMF Working Papers 15/74, International Monetary Fund.
    2. Jan Brùha, 2011. "An Empirical Small Labor Market Model for the Czech Economy," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 61(5), pages 434-449, November.
    3. Piotr Białowolski & Tomasz Kuszewski & Bartosz Witkowski, 2014. "Bayesian averaging of classical estimates in forecasting macroeconomic indicators with application of business survey data," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 41(1), pages 53-68, February.
    4. 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.

    More about this item

    Keywords

    Economic models; Economic indicators; Economic forecasting; Monetary policy; Forecasting; High frequency indicators; inflation; equation; prediction; equations;

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