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On economic evaluation of directional forecasts

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  • Oliver Blaskowitz
  • Helmut Herwartz

Abstract

It is commonly accepted that information is helpful if it can be exploited to improve a decision mak- ing process. In economics, decisions are often based on forecasts of up{ or downward movements of the variable of interest. We point out that directional forecasts can provide a useful framework to assess the economic forecast value when loss functions (or success measures) are properly formu- lated to account for realized signs and realized magnitudes of directional movements. We discuss a general approach to evaluate (directional) forecasts which is simple to implement, robust to outlying or unreasonable forecasts and which provides an economically interpretable loss/success functional framework. As such, the measure of directional forecast value is a readily available alternative to the commonly used squared error loss criterion.

Suggested Citation

  • Oliver Blaskowitz & Helmut Herwartz, 2009. "On economic evaluation of directional forecasts," SFB 649 Discussion Papers SFB649DP2009-052, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2009-052
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    References listed on IDEAS

    as
    1. Oliver Blaskowitz & Helmut Herwartz, 2009. "Adaptive forecasting of the EURIBOR swap term structure," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 575-594.
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    Cited by:

    1. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 4181, WU Vienna University of Economics and Business.
    2. Helmut Herwartz & Konstantin A. Kholodilin, 2014. "In‐Sample and Out‐of‐Sample Prediction of stock Market Bubbles: Cross‐Sectional Evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 15-31, January.
    3. Emilian Dobrescu, 2014. "Attempting to Quantify the Accuracy of Complex Macroeconomic Forecasts," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-21, December.
    4. repec:eee:intfor:v:33:y:2017:i:3:p:569-580 is not listed on IDEAS
    5. Christian Pierdzioch & Monique B. Reid & Rangan Gupta, 2014. "On the Directional Accuracy of Inflation Forecasts: Evidence from South African Survey Data," Working Papers 24/2014, Stellenbosch University, Department of Economics.
    6. Bergmeir, Christoph & Costantini, Mauro & Benítez, José M., 2014. "On the usefulness of cross-validation for directional forecast evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 132-143.
    7. Ince, Onur & Molodtsova, Tanya, 2017. "Rationality and forecasting accuracy of exchange rate expectations: Evidence from survey-based forecasts," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 47(C), pages 131-151.
    8. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, Elsevier.
    9. Tsuchiya, Yoichi, 2014. "Purchasing and supply managers provide early clues on the direction of the US economy: An application of a new market-timing test," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 599-618.
    10. Crespo Cuaresma, Jesus & Fortin, Ines & Hlouskova, Jaroslava, 2017. "Exchange rate forecasting and the performance of currency portfolios," Economics Series 326, Institute for Advanced Studies.
    11. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    12. Francisco Javier Eransus & Alfonso Novales Cinca, 2014. "Parameter Estimation Error in Tests of Predictive Performance under Discrete Loss Functions," Documentos de Trabajo del ICAE 2014-22, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.

    More about this item

    Keywords

    Directional forecasts; directional forecast value; forecast evaluation; economic forecast value; mean squared forecast error; mean absolute forecast error;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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