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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

QuantileRegression

By: Ashley Nissenbaum


About the author
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
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


Earnings and skewness
Earnings and Skewness

  • Linear Regression

    • Focuses on the behavior of the conditional mean of the dependent variable

    • Most people make under $300K per event


Reasons for skewness
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


Tiger woods
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


Concept of quantile regression
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


Covariates
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.


Empirical results
Empirical Results

  • Simple level OLS (Ordinary Least Squares) regression estimate:


Ols and quantile regression results
OLS and Quantile Regression Results



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