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Combining Judgment and Models

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  • FRANCESCA MONTI

Abstract

This paper proposes a parsimonious and model‐consistent method for combining forecasts generated by structural microfounded models and judgmental forecasts. The method delivers along several dimensions. First, it improves the forecasting performance of the model. Second, it allows interpreting the judgmental forecasts through the lens of the model. Finally, it provides a framework to assess the informational content of the judgmental forecasters. I illustrate the proposed methodology with a real‐time forecasting exercise using a simple neo‐Keynesian dynamic stochastic general equilibrium model and the Survey of Professional Forecasters.

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  • Francesca Monti, 2010. "Combining Judgment and Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(8), pages 1641-1662, December.
    • Handle: RePEc:wly:jmoncb:v:42:y:2010:i:8:p:1641-1662
    DOI: 10.1111/j.1538-4616.2010.00357.x
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    Cited by:

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    2. Drautzburg, Thorsten, 2024. "A structural approach to combining external and DSGE model forecasts," Economics Letters, Elsevier, vol. 235(C).
    3. Carlos Madeira & João Madeira & Paulo Santos Monteiro, 2023. "The origins of monetary policy disagreement: the role of supply and demand shocks," BIS Working Papers 1118, Bank for International Settlements.
    4. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    5. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    6. Ilhan Kilic & Faruk Balli, 2024. "Measuring economic country-specific uncertainty in Türkiye," Empirical Economics, Springer, vol. 67(4), pages 1649-1689, October.
    7. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    8. Giannone, Domenico & Monti, Francesca & Reichlin, Lucrezia, 2016. "Exploiting the monthly data flow in structural forecasting," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 201-215.
    9. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    10. Gelain, Paolo & Manganelli, Simone, 2020. "Monetary policy with judgment," Working Paper Series 2404, European Central Bank.
    11. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    12. Smets, Frank & Warne, Anders & Wouters, Rafael, 2014. "Professional forecasters and real-time forecasting with a DSGE model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 981-995.
    13. Burgess, Stephen & Fernandez-Corugedo, Emilio & Groth, Charlotta & Harrison, Richard & Monti, Francesca & Theodoridis, Konstantinos & Waldron, Matt, 2013. "The Bank of England's forecasting platform: COMPASS, MAPS, EASE and the suite of models," Bank of England working papers 471, Bank of England.
    14. Yuliya Rychalovska & Sergey Slobodyan & Raf Wouters, 2024. "Survey Expectations, Adaptive Learning and Inflation Dynamics," CERGE-EI Working Papers wp781, The Center for Economic Research and Graduate Education - Economics Institute, Prague.

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