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Conditional forecasts in DSGE models

Author

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  • Junior Maih

    (Norges Bank (Central Bank of Norway))

Abstract

New-generation DSGE models are sometimes misspecified in dimensions that matter for their forecasting performance. The paper suggests one way to improve the forecasts of a DSGE model using a conditioning information that need not be accurate. The technique presented allows for agents to anticipate the information on the conditioning variables several periods ahead. It also allows the forecaster to apply a continuum of degrees of uncertainty around the mean of the conditioning information, making hard-conditional and unconditional forecasts special cases. An application to a small open-economy DSGE model shows that the benefits of conditioning depend crucially on the ability of the model to capture the correlation between the conditioning information and the variables of interest.

Suggested Citation

  • Junior Maih, 2010. "Conditional forecasts in DSGE models," Working Paper 2010/07, Norges Bank.
    • Handle: RePEc:bno:worpap:2010_07
    Note: First version:
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    File URL: https://www.norges-bank.no/en/news-events/news-publications/Papers/Working-Papers/2010/WP-201007/
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Conditional forecasts in DSGE models
      by Christian Zimmermann in NEP-DGE blog on 2010-06-07 07:43:04

    Citations

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    Cited by:

    1. Andrew Binning & Junior Maih, 2016. "Forecast uncertainty in the neighborhood of the effective lower bound: How much asymmetry should we expect?," Working Paper 2016/13, Norges Bank.
    2. Andrew Binning & Junior Maih, 2016. "Implementing the zero lower bound in an estimated regime-switching DSGE model," Working Paper 2016/3, Norges Bank.
    3. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    4. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    5. Canova, Fabio & Ferroni, Filippo, 2020. "A hitchhiker guide to empirical macro models," CEPR Discussion Papers 15446, C.E.P.R. Discussion Papers.
    6. Eyal Argov & Emanuel Barnea & Alon Binyamini & Eliezer Borenstein & David Elkayam & Irit Rozenshtrom, 2012. "MOISE: A DSGE Model for the Israeli Economy," Bank of Israel Working Papers 2012.06, Bank of Israel.
    7. Andrew Binning, 2022. "An Efficient Application of the Extended Path Algorithm in Matlab with Examples," Treasury Working Paper Series 22/02, New Zealand Treasury.
    8. Michael W. McCracken & Joseph T. McGillicuddy & Michael T. Owyang, 2022. "Binary Conditional Forecasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1246-1258, June.
    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. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint Prediction Bands for Macroeconomic Risk Management," Working Papers No 5/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    11. Linde, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks' Macro Models," CEPR Discussion Papers 11405, C.E.P.R. Discussion Papers.
    12. Andersson, Michael K. & Palmqvist, Stefan & Waggoner, Daniel F., 2010. "Density-Conditional Forecasts in Dynamic Multivariate Models," Working Paper Series 247, Sveriges Riksbank (Central Bank of Sweden).
    13. Andrés González & Lavan Mahadeva & Diego Rodríguez & Luis Rojas, 2011. "Overcoming the Forecasting Limitations of Forward-Looking Theory Based Models," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 29(66), pages 246-294, December.
    14. Lindé, J. & Smets, F. & Wouters, R., 2016. "Challenges for Central Banks’ Macro Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2185-2262, Elsevier.

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    More about this item

    Keywords

    DSGE model; conditional forecast;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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