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Professional Forecasters: How to Understand and Exploit Them Through a DSGE Model

Author

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  • Luis E. Rojas
Abstract
This paper derives a link between the forecasts of professional forecasters and a DSGE model. I show that the forecasts of a professional forecaster can be incorporated to the state space representation of the model by allowing the measurement error of the forecast and the structural shocks to be correlated. The parameters capturing this correlation are reduced form parameters that allow to address two issues i) How the forecasts of the professional forecaster can be exploited as a source of information for the estimation of the model and ii) How to characterize the deviations of the professional forecaster from an ideal complete information forecaster in terms of the shocks and the structure of the economy.

Suggested Citation

  • Luis E. Rojas, 2011. "Professional Forecasters: How to Understand and Exploit Them Through a DSGE Model," Borradores de Economia 664, Banco de la Republica de Colombia.
    • Handle: RePEc:bdr:borrec:664
    DOI: 10.32468/be.664
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    References listed on IDEAS

    as
    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2007. "The ECB survey of professional forecasters (SPF) – A review after eight years’ experience," Occasional Paper Series 59, European Central Bank.
    3. Andrade, Philippe & Le Bihan, Hervé, 2013. "Inattentive professional forecasters," Journal of Monetary Economics, Elsevier, vol. 60(8), pages 967-982.
    4. Andrés González Gómez & Lavan Mahadeva & Diego Rodríguez & Luis Eduardo Rojas, 2009. "Monetary Policy Forecasting In A Dsge Model With Data That Is Uncertain, Unbalanced And About The Future," Borradores de Economia 5480, Banco de la Republica.
    5. Gonzalo Fernández-de-Córdoba & José Torres, 2011. "Forecasting the Spanish economy with an augmented VAR–DSGE model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(3), pages 379-399, September.
    6. Bartosz Mackowiak & Mirko Wiederholt, 2009. "Optimal Sticky Prices under Rational Inattention," American Economic Review, American Economic Association, vol. 99(3), pages 769-803, June.
    7. Marc P. Giannoni & Jean Boivin, 2005. "DSGE Models in a Data-Rich Environment," Computing in Economics and Finance 2005 431, Society for Computational Economics.
    8. Kenny, Geoff & Genre, Véronique & Bowles, Carlos & Friz, Roberta & Meyler, Aidan & Rautanen, Tuomas, 2007. "The ECB survey of professional forecasters (SPF) - A review after eight years' experience," Occasional Paper Series 59, European Central Bank.
    9. Kenny, Geoff & Genre, Véronique & Meyler, Aidan & Timmermann, Allan, 2010. "Combining the forecasts in the ECB survey of professional forecasters: can anything beat the simple average?," Working Paper Series 1277, European Central Bank.
    10. Koopman, Siem Jan & Harvey, Andrew, 2003. "Computing observation weights for signal extraction and filtering," Journal of Economic Dynamics and Control, Elsevier, vol. 27(7), pages 1317-1333, May.
    11. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-287, April.
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    More about this item

    Keywords

    Professional Forecasters; DSGE models.;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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