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Testing the value of directional forecasts in the presence of serial correlation

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  • Blaskowitz, Oliver
  • Herwartz, Helmut

Abstract

Common approaches to testing the economic value of directional forecasts are based on the classical χ2-test for independence, Fisher’s exact test or the Pesaran and Timmermann test for market timing. These tests are asymptotically valid for serially independent observations, but in the presence of serial correlation they are markedly oversized, as has been confirmed in a simulation study. We therefore summarize robust test procedures for serial correlation and propose a bootstrap approach, the relative merits of which we illustrate by means of a Monte Carlo study. Our evaluations of directional predictions of stock returns and changes in Euribor rates demonstrate the importance of accounting for serial correlation in economic time series when making such predictions.

Suggested Citation

  • Blaskowitz, Oliver & Herwartz, Helmut, 2014. "Testing the value of directional forecasts in the presence of serial correlation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 30-42.
    • Handle: RePEc:eee:intfor:v:30:y:2014:i:1:p:30-42
    DOI: 10.1016/j.ijforecast.2013.06.001
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    Cited by:

    1. 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.
    2. Hartmann, Matthias & Herwartz, Helmut & Ulm, Maren, 2017. "A comparative assessment of alternative ex ante measures of inflation uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 76-89.
    3. 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.
    4. 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.

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