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Quantile Regression for Sports Economics

Author

Listed:
  • Michael A. Leeds

    (Temple University)

Abstract

Quantile regression provides sports economists with a powerful research tool. Unlike least squares, it is not tied to restrictive assumptions about the distribution of the error term, which makes it particularly valuable in settings with highly skewed distributions, like sports labor markets. It allows investigators to check for heteroskedasticity and to avoid censored variable bias. Researchers can use it simulate the distribution of incomes or profits, not just their mean values. Still, few sports economists use quantile regression, and, when used, it is frequently misinterpreted. This article provides a user-friendly introduction to quantile regression that will stimulate its use in the sports economics literature.

Suggested Citation

  • Michael A. Leeds, 2014. "Quantile Regression for Sports Economics," International Journal of Sport Finance, Fitness Information Technology, vol. 9(4), pages 346-359, November.
    • Handle: RePEc:jsf:intjsf:v:9:y:2014:i:4:p:346-359
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    More about this item

    Keywords

    quantile regression; conditional distribution; counterfactual distribution;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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