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Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament


  • Steven Caudill
  • Norman Godwin


Several authors have recently explored the estimation of binary choice models based on asymmetric error structures. One such family of skewed models is based on the exponential generalized beta type 2 (EGB2). One model in this family is the skewed logit. Recently, McDonald (1996, 2000) extended the work on the EGB2 family of skewed models to permit heterogeneity in the scale parameter. The aim of this paper is to extend the skewed logit model to allow for heterogeneity in the skewness parameter. By this we mean that, in the model developed, here the skewness parameter is permitted to vary from observation to observation by making it a function of exogenous variables. To demonstrate the usefulness of our model, we examine the issue of the predictive ability of sports seedings. We find that we are able to obtain better probability predictions using the skewed logit model with heterogeneous skewness than can be obtained with logit, probit, or skewed logit.

Suggested Citation

  • Steven Caudill & Norman Godwin, 2002. "Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 991-1001.
  • Handle: RePEc:taf:japsta:v:29:y:2002:i:7:p:991-1001 DOI: 10.1080/0266476022000006694

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    References listed on IDEAS

    1. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665 National Bureau of Economic Research, Inc.
    2. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
    3. McDonald, James B., 1996. "An application and comparison of some flexible parametric and semi-parametric qualitative response models," Economics Letters, Elsevier, vol. 53(2), pages 145-152, November.
    4. Barniv, Ran & McDonald, James B, 1999. "Review of Categorical Models for Classification Issues in Accounting and Finance," Review of Quantitative Finance and Accounting, Springer, vol. 13(1), pages 39-62, July.
    5. Clarke, Darral G. & McDonald, James B., 1992. "Generalized bankruptcy models applied to predicting consumer credit behavior," Journal of Economics and Business, Elsevier, vol. 44(1), pages 47-62, February.
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    Cited by:

    1. Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
      • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    2. Vaughan Williams, Leighton & Stekler, Herman O., 2010. "Sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 445-447, July.
      • Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, Research Program on Forecasting, revised Jan 2007.
    3. Gray Kathy L. & Schwertman Neil C., 2012. "Comparing Team Selection and Seeding for the 2011 NCAA Men's Basketball Tournament," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-13, March.
    4. Morris Tracy L. & Bokhari Faryal H., 2012. "The Dreaded Middle Seeds - Are They the Worst Seeds in the NCAA Basketball Tournament?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(2), pages 1-13, June.
    5. Franklin Mixon, Jr. & Steven Caudill & Christopher Duquette, 2008. "The impact of money on elections: evidence from open seat races in the United States House of Representatives, 1990-2004," Economics Bulletin, AccessEcon, vol. 4(2), pages 1-12.

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