Quantile Regression for Sports Economics

Michael Leeds

International Journal of Sport Finance, 2014, vol. 9, issue 4, 346-359

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.

Keywords: quantile regression; conditional distribution; counterfactual distribution (search for similar items in EconPapers)
JEL-codes: L83 (search for similar items in EconPapers)
Date: 2014
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International Journal of Sport Finance is currently edited by Arne Feddersen, Babatunde Buraimo, Joachim Prinz and Jane Ruseski

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