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Forecasting Binary Outcomes

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  • Kajal Lahiri
  • Liu Yang

Abstract

Binary events are involved in many economic decision problems. In recent years, considerable progress has been made in diverse disciplines in developing models for forecasting binary outcomes. We distinguish between two types of forecasts for binary events that are generally obtained as the output of regression models: probability forecasts and point forecasts. We summarize specification, estimation, and evaluation of binary response models for the purpose of forecasting in a unified framework which is characterized by the joint distribution of forecasts and actuals, and a general loss function. Analysis of both the skill and the value of probability and point forecasts can be carried out within this framework. Parametric, semiparametric, nonparametric, and Bayesian approaches are covered. The emphasis is on the basic intuitions underlying each methodology, abstracting away from the mathematical details.

Suggested Citation

  • Kajal Lahiri & Liu Yang, 2012. "Forecasting Binary Outcomes," Discussion Papers 12-09, University at Albany, SUNY, Department of Economics.
  • Handle: RePEc:nya:albaec:12-09
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    3. Hwang, Youngjin, 2019. "Forecasting recessions with time-varying models," Journal of Macroeconomics, Elsevier, vol. 62(C).
    4. 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.
    5. Walter Kraemer & Simon Neumärker, 2016. "Comparing Default Predictions in the Rating Industry for Different Sets of Obligors," CESifo Working Paper Series 5768, CESifo.
    6. Bago d'Uva, Teresa & O'Donnell, Owen & van Doorslaer, Eddy, 2020. "Who can predict their own demise? Heterogeneity in the accuracy and value of longevity expectations☆," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    7. Thitithep Sitthiyot & Kanyarat Holasut, 2022. "On the Evaluation of Skill in Binary Forecast," Papers 2209.04686, arXiv.org.
    8. Krämer, Walter & Neumärker, Simon, 2016. "Comparing the accuracy of default predictions in the rating industry for different sets of obligors," Economics Letters, Elsevier, vol. 145(C), pages 48-51.
    9. Kajal Lahiri & Cheng Yang, 2022. "ROC approach to forecasting recessions using daily yield spreads," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(4), pages 191-203, October.
    10. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
    11. S. Borağan Aruoba & Allan Drazen & Razvan Vlaicu, 2019. "A Structural Model Of Electoral Accountability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 60(2), pages 517-545, May.
    12. Lahiri, Kajal & Yang, Liu, 2016. "Asymptotic variance of Brier (skill) score in the presence of serial correlation," Economics Letters, Elsevier, vol. 141(C), pages 125-129.
    13. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    14. Kajal Lahiri & Cheng Yang, 2023. "ROC and PRC Approaches to Evaluate Recession Forecasts," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 119-148, September.
    15. Krämer, Walter & Neumärker, Simon, 2019. "Skill Scores and modified Lorenz domination in default forecasts," Economics Letters, Elsevier, vol. 181(C), pages 61-64.
    16. Lahiri, Kajal & Yang, Liu, 2015. "A further analysis of the conference board’s new Leading Economic Index," International Journal of Forecasting, Elsevier, vol. 31(2), pages 446-453.
    17. Dendramis, Y. & Tzavalis, E. & Adraktas, G., 2018. "Credit risk modelling under recessionary and financially distressed conditions," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 160-175.

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