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Confidence Bands for ROC Curves with Serially Dependent Data

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  • Kajal Lahiri
  • Liu Yang

Abstract

We propose serial correlation robust asymptotic confidence bands for the receiver operating characteristic (ROC) curves estimated by quasi-maximum likelihood in the binormal model. Our simulation experiments confirm that this new method performs fairly well in finite samples. The conventional procedure is found to be markedly undersized in terms of yielding empirical coverage probabilities lower than the nominal level, especially when the serial correlation is strong. We evaluate the three-quarter-ahead probability forecasts for real GDP declines from the Survey of Professional Forecasters, and find that one would draw a misleading conclusion about forecasting skill if serial correlation is ignored.

Suggested Citation

  • Kajal Lahiri & Liu Yang, 2013. "Confidence Bands for ROC Curves with Serially Dependent Data," Discussion Papers 13-07, University at Albany, SUNY, Department of Economics.
    • Handle: RePEc:nya:albaec:13-07
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    File URL: http://www.albany.edu/economics/research/workingp/2013/ly_roc.pdf
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    1. Peter G. Hall & Rob J. Hyndman & Yanan Fan, 2003. "Non Parametric Confidence Intervals for Receiver Operating Characteristic Curves," Monash Econometrics and Business Statistics Working Papers 12/03, Monash University, Department of Econometrics and Business Statistics.
    2. Gorr, Wilpen L. & Schneider, Matthew J., 2013. "Large-change forecast accuracy: Reanalysis of M3-Competition data using receiver operating characteristic analysis," International Journal of Forecasting, Elsevier, vol. 29(2), pages 274-281.
    3. Yixiao Sun, 2013. "A heteroskedasticity and autocorrelation robust F test using an orthonormal series variance estimator," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-26, February.
    4. James A. Hanley, 1988. "The Robustness of the "Binormal" Assumptions Used in Fitting ROC Curves," Medical Decision Making, , vol. 8(3), pages 197-203, August.
    5. Yixiao Sun & Peter C. B. Phillips & Sainan Jin, 2008. "Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing," Econometrica, Econometric Society, vol. 76(1), pages 175-194, January.
    6. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    7. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    8. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    9. Òscar Jordà & Moritz Schularick & Alan M Taylor, 2011. "Financial Crises, Credit Booms, and External Imbalances: 140 Years of Lessons," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 59(2), pages 340-378, June.
    10. Sun, Yixiao, 2014. "Let’s fix it: Fixed-b asymptotics versus small-b asymptotics in heteroskedasticity and autocorrelation robust inference," Journal of Econometrics, Elsevier, vol. 178(P3), pages 659-677.
    11. Peter Hall, 2004. "Nonparametric confidence intervals for receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 91(3), pages 743-750, September.
    12. Stephen Satchel & Wei Xia, 2006. "Analytic Models of the ROC Curve: Applications to Credit Rating Model Validation," Research Paper Series 181, Quantitative Finance Research Centre, University of Technology, Sydney.
    13. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    14. Cohen, Jacqueline & Garman, Samuel & Gorr, Wilpen, 2009. "Empirical calibration of time series monitoring methods using receiver operating characteristic curves," International Journal of Forecasting, Elsevier, vol. 25(3), pages 484-497, July.
    15. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    16. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    17. Blaskowitz, Oliver J. & Herwartz, Helmut, 2008. "Testing directional forecast value in the presence of serial correlation," SFB 649 Discussion Papers 2008-073, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Kiefer, Nicholas M. & Vogelsang, Timothy J., 2005. "A New Asymptotic Theory For Heteroskedasticity-Autocorrelation Robust Tests," Econometric Theory, Cambridge University Press, vol. 21(6), pages 1130-1164, December.
    19. Eugene Demidenko, 2012. "Confidence intervals and bands for the binormal ROC curve revisited," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 67-79, March.
    20. Zeileis, Achim, 2004. "Econometric Computing with HC and HAC Covariance Matrix Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 11(i10).
    21. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    22. 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.
    23. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    24. Zeileis, Achim, 2006. "Object-oriented Computation of Sandwich Estimators," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 16(i09).
    25. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    26. Stein, Roger M., 2005. "The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing," Journal of Banking & Finance, Elsevier, vol. 29(5), pages 1213-1236, May.
    27. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    28. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    29. Guangqin Ma & W.J. Hall, 1993. "Confidence Bands for Receiver Operating Characteristic Curves," Medical Decision Making, , vol. 13(3), pages 191-197, August.
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    Cited by:

    1. Lahiri Kajal & Yang Liu, 2016. "A non-linear forecast combination procedure for binary outcomes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 421-440, September.
    2. Máximo Camacho & Gonzalo Palmieri, 2021. "Evaluating the OECD’s main economic indicators at anticipating recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 80-93, January.
    3. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    4. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.

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