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Understanding Economic Forecasts

Editor

Listed:
  • David F. Hendry
    (Oxford University)

  • Neil R. Ericsson
    (Federal Reserve Board)

Abstract

Historically, the theory of forecasting that underpinned actual practice in economics has been based on two key assumptions -- that the model was a good representation of the economy and that the structure of the economy would remain relatively unchanged. In reality, forecast models are mis-specified, the economy is subject to unanticipated shifts, and the failure to make accurate predictions is relatively common. In the last decade, economists have developed new theories of economic forecasting and additional methods of forecast evaluation that make less stringent assumptions. These theories and methods acknowledge that the economy is dynamic and prone to sudden shifts. They also recognize that forecasting models, however good, are greatly simplified representations that will be incorrect in some respects. One advantage of these newer approaches is that we can now account for the different results of competing forecasts. In this book, academic specialists, practitioners, and a financial journalist explain these new developments in economic forecasting. The authors discuss how forecasting is conducted, evaluated, reported, and applied by academic, private, and governmental bodies, as well as how forecasting might be taught and what costs are induced by forecast errors. They also describe how econometric models for forecasting are constructed, how properties of forecasting methods can be analyzed, and what the future of economic forecasting may bring.

Suggested Citation

  • David F. Hendry & Neil R. Ericsson (ed.), 2003. "Understanding Economic Forecasts," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262582422, December.
  • Handle: RePEc:mtp:titles:0262582422
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    Citations

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    Cited by:

    1. Wändi Bruine de Bruin & Michael F. Bryan & Simon M. Potter & Giorgio Topa & Wilbert Van der Klaauw, 2008. "Rethinking the measurement of household inflation expectations: preliminary findings," Staff Reports 359, Federal Reserve Bank of New York.
    2. Rolando F. Peláez, 2015. "A recession‐and‐state forecasting model," Southern Economic Journal, John Wiley & Sons, vol. 81(4), pages 1025-1039, April.
    3. Katarina Juselius, 2022. "A Theory-Consistent CVAR Scenario for a Monetary Model with Forward-Looking Expectations," Econometrics, MDPI, vol. 10(2), pages 1-15, April.
    4. Roland Döhrn, 2011. "Konjunkturprognosen in bewegten Zeiten: Die Kunst des Unmöglichen?," RWI Materialien, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, pages 26, 01.
    5. Peláez, Rolando F., 2015. "Market-timing the business cycle," Review of Financial Economics, Elsevier, vol. 26(C), pages 55-64.
    6. Gerit Vogt, 2010. "Zur Güte der ifo Dresden Konjunkturprognosen," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(01), pages 28-32, 02.
    7. Goodwin, Paul & Önkal, Dilek & Thomson, Mary, 2010. "Do forecasts expressed as prediction intervals improve production planning decisions?," European Journal of Operational Research, Elsevier, vol. 205(1), pages 195-201, August.
    8. Döhrn, Roland, 2010. "Konjunkturprognosen in bewegten Zeiten: Die Kunst des Unmöglichen?," RWI Materialien 62, RWI - Leibniz-Institut für Wirtschaftsforschung.
    9. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.
    10. Rolando F. Peláez, 2015. "Market‐timing the business cycle," Review of Financial Economics, John Wiley & Sons, vol. 26(1), pages 55-64, September.
    11. Araz Taeihagh, 2017. "Crowdsourcing: a new tool for policy-making?," Policy Sciences, Springer;Society of Policy Sciences, vol. 50(4), pages 629-647, December.
    12. Gerit Vogt, 2009. "Konjunkturprognose in Deutschland. Ein Beitrag zur Prognose der gesamtwirtschaftlichen Entwicklung auf Bundes- und Länderebene," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 36.
    13. J. M. Kargbo, 2007. "Forecasting agricultural exports and imports in South Africa," Applied Economics, Taylor & Francis Journals, vol. 39(16), pages 2069-2084.
    14. repec:zbw:rwimat:062 is not listed on IDEAS

    More about this item

    Keywords

    economic forecasting; econometric models;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology

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