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Quantile Regression By: Ashley Nissenbaum. About the Author Leo H. Kahane Associate Professor at Providence College Research Sport economics, international.

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Presentation on theme: "Quantile Regression By: Ashley Nissenbaum. About the Author Leo H. Kahane Associate Professor at Providence College Research Sport economics, international."— Presentation transcript:

1 Quantile Regression By: Ashley Nissenbaum

2 About the Author Leo H. Kahane Associate Professor at Providence College Research Sport economics, international trade, political science Editor of Journal of Sports Economics

3 Previous Research Golf earnings are highly positively skewed Schmanske (1992) Value of the marginal product from putting may be in the range of $500 per hour of practice. Alexander and Kern (2005) “Drive for show, putt for dough” Callan and Thomas (2007) Skills determine score, which determines rank and thus earnings

4 Earnings and Skewness Linear Regression Focuses on the behavior of the conditional mean of the dependent variable Most people make under $300K per event

5 Reasons for Skewness Payout Structure Non-linear Top 50% after the first two rounds: 1 st place receives 18%, 2 nd place receives 10.8%, 3 rd place receives 6.8%, 4 th place – 4.8%, etc Extraordinary Talented Golfers Tournament wins are spread across a large number of golfers

6 Tiger Woods Won 185 tournaments 14 professional major tournaments, 71 PGA Tour events $500 Million net worth Highest paid athlete from 2001 to 2012 $132 million from tournaments

7 Concept of Quantile Regression Equation for Quantile Regression: Where: y(i)= real earnings per PGA event Q= Specific quantile associated with the equation Β = Vector of coefficients to be estimated Ε = Error term X(i)= Covariates

8 Covariates x(i) = covariates expected to explain golf earnings Greens in regulation The percent of time a player was able to hit the green in regulation (greens hit in regulation / holes played x 100). Positive correlation expected. Putting average Average number of putts needed to finish a hole per green hit in regulation. Negative correlation expected. Save percentage Percentage of time a golfer was able to get the ball in the hole in two shots or less following landing in a greenside sand bunker (regardless of score). Positive correlation expected. Yards per drive Average number of yards per measured drive. Positive correlation expected. Driving accuracy Percentage of time a tee shot comes to rest in the fairway. Positive correlation expected.

9 Empirical Results Simple level OLS (Ordinary Least Squares) regression estimate:

10 OLS and Quantile Regression Results

11 Coefficients Graph


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