Quantile regression
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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

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QuantileRegression

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

  • 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

  • Linear Regression

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

    • Most people make under $300K per event


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

  • 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

  • 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

  • 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

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


OLS and Quantile Regression Results


Coefficients Graph


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