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Quantile Regression

Quantile Regression . By: Ashley Nissenbaum. About the Author . Leo H. Kahane Associate Professor at Providence College Research Sport economics, international trade, political science Editor of Journal of Sports Economics. Previous Research . Golf earnings are highly positively skewed

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Quantile Regression

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  1. QuantileRegression 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: 1st place receives 18%, 2nd place receives 10.8%, 3rd place receives 6.8%, 4th 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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