Negotiating Salaries through Quantile Regression
Estimating the salaries of professional athletes has received a substantial amount of attention both in the press and in academic journals. A statistical technique that can be used to obtain an estimate of a player's salary with a given set of performance characteristics is the classical least squares regression analysis. This technique does not work well, however, if the data upon which the model is based contain outliers or are not normally distributed. In this paper we focus our attention on the salaries of American League baseball players in 2007 and demonstrate the usefulness of an alternative estimation approach that of quantile regression analysis. Our results indicate that ordinary least squares regression overestimates the salaries of poor players, and underestimates the salaries of star players. This, we believe, is a compelling reason to apply quantile regression in the prediction of baseball player salaries.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 6 (2010)
Issue (Month): 1 (January)
|Contact details of provider:|| Web page: https://www.degruyter.com|
|Order Information:||Web: https://www.degruyter.com/view/j/jqas|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jahn Hakes & Chad Turner, 2011.
"Pay, productivity and aging in Major League Baseball,"
Journal of Productivity Analysis,
Springer, vol. 35(1), pages 61-74, February.
- Turner, Chad & Hakes, Jahn, 2007. "Pay, productivity and aging in Major League Baseball," MPRA Paper 4326, University Library of Munich, Germany.
- Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
- Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
- Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521608275, February.
- Jahn K. Hakes & Raymond D. Sauer, 2006. "An Economic Evaluation of the Moneyball Hypothesis," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 173-186, Summer.
- Anthony C. Krautmann & Margaret Oppenheimer, 2002. "Contract Length and the Return to Performance in Major League Baseball," Journal of Sports Economics, , vol. 3(1), pages 6-17, February.
- Herbert F. Lewis & Thomas R. Sexton & Kathleen A. Lock, 2007. "Player Salaries, Organizational Efficiency, and Competitiveness in Major League Baseball," Journal of Sports Economics, , vol. 8(3), pages 266-294, June. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:6:y:2010:i:1:n:7. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.