1. Quantile Regression

2. The Problem The Estimator Computation Properties of the Regression Properties of the Estimator Hypothesis Testing Bibliography Software

3. Quantile Regression The Problem

4. Quantile Regression Problem –The distribution of Y, the “dependent” variable, conditional on the covariate X, may have thick tails. –The conditional distribution of Y may be asymmetric. –The conditional distribution of Y may not be unimodal. Neither regression nor ANOVA will give us robust results. Outliers are problematic, the mean is pulled toward the skewed tail, multiple modes will not be revealed.

5. Quantile Regression Problem –ANOVA and regression provide information only about the conditional mean. –More knowledge about the distribution of the statistic may be important. –The covariates may shift not only the location or scale of the distribution, they may affect the shape as well.

6. Quantile Regression

9. The Estimator

10. Quantile Regression Ordinarily we specify a quadratic loss function. That is, L(u) = u 2 Under quadratic loss we use the conditional mean, via regression or ANOVA, as our predictor of Y for a given X=x.

11. Quantile Regression Definition: Given p ∈ [0, 1]. A pth quantile of a random variable Z is any number ζ p such that Pr(Z< ζ p ) ≤ p ≤ Pr(Z ≤ ζ p ). The solution always exists, but need not be unique. Ex: Suppose Z={3, 4, 7, 9, 9, 11, 17, 21} and p=0.5 then Pr(Z<9) = 3/8 ≤ 1/2 ≤ Pr(Z ≤ 9) = 5/8

12. Quantile Regression Quantiles can be used to characterize a distribution: –Median –Interquartile Range –Interdecile Range –Symmetry = (ζ.75 - ζ.5 )/(ζ.5 - ζ.25 ) –Tail Weight = (ζ.90 - ζ.10 )/(ζ.75 - ζ.25 )

13. Quantile Regression Suppose Z is a continuous r.v. with cdf F(.), then Pr(Z<z) = Pr(Z≤z)=F(z) for every z in the support and a pth quantile is any number ζ p such that F(ζ p ) = p If F is continuous and strictly increasing then the inverse exists and ζ p =F -1 (p)

14. Quantile Regression Definition: The asymmetric absolute loss function is Where u is the prediction error we have made and I(u) is an indicator function of the sort

15. Quantile Regression Absolute Loss vs. Quadratic Loss

16. Quantile Regression Proposition: Under the asymmetric absolute loss function L p a best predictor of Y given X=x is a p th conditional quantile. For example, if p=.5 then the best predictor is the median.

17. Quantile Regression Definition: A parametric quantile regression model is correctly specified if, for example, That is, is a particular linear combination of the independent variable(s) such that where F( ) is some univariate distribution.

18. Quantile Regression.25

19. Quantile Regression Definition: A quantile regression model is identifiable if has a unique solution.

20. Quantile Regression A family of conditional quantiles of Y given X=x. –Let Y=α + βx + u with α = β = 1

21. Quantile Regression

24. Quantiles at.9,.75,.5,.25, and.10. Today’s temperature fitted to a quartic on yesterday’s temperature.

25. Quantile Regression Computation of the Estimate

26. Quantile Regression Estimation The quantile regression coefficients are the solution to The k first order conditions are

27. Quantile Regression Estimation The fitted line will go through k data points. The # of negative residuals ≤ np ≤ # of neg residuals + # of zero residuals The computational algorithm is to set up the objective function as a linear programming problem The solution to (1) – (2) [previous slide] need not be unique.

28. Quantile Regression Properties of the Regression

29. Quantile Regression Properties of the regression Transformation equivariance For any monotone function, h(.), since P(T<t|x) = P(h(T)<h(t)|x). This is especially important where the response variable has been censored, I.e. top coded.

30. Properties The mean does not have transformation equivariance since Eh(Y) ≠ h(E(Y))

31. Quantile Regression Equivariance Practical implications

32. Equivariance (i) and (ii) imply scale equivariance (iii) is a shift or regression equivariance (iv) is equivariance to reparameterization of design

33. Quantile Regression Properties Robust to outliers. As long as the sign of the residual does not change, any Y i may be changed without shifting the conditional quantile line. The regression quantiles are correlated.

34. Quantile Regression Properties of the Estimator

35. Quantile Regression Properties of the Estimator Asymptotic Distribution The covariance depends on the unknown f(.) and the value of the vector x at which the covariance is being evaluated.

36. Quantile Regression Properties of the Estimator When the error is independent of x then the coefficient covariance reduces to where

37. Quantile Regression Properties of the Estimator In general the quantile regression estimator is more efficient than OLS The efficient estimator requires knowledge of the true error distribution.

38. Quantile Regression Coefficient Interpretation The marginal change in the Θth conditional quantile due to a marginal change in the jth element of x. There is no guarantee that the ith person will remain in the same quantile after her x is changed.

39. Quantile Regression Hypothesis Testing

40. Quantile Regression Hypothesis Testing Given asymptotic normality, one can construct asymptotic t-statistics for the coefficients The error term may be heteroscedastic. The test statistic is, in construction, similar to the Wald Test. A test for symmetry, also resembling a Wald Test, can be built relying on the invariance properties referred to above.

41. Heteroscedasticity Model: y i = β o +β 1 x i +u i, with iid errors. –The quantiles are a vertical shift of one another. Model: y i = β o +β 1 x i +σ(x i )u i, errors are now heteroscedastic. –The quantiles now exhibit a location shift as well as a scale shift. Khmaladze-Koenker Test Statistic

42. Quantile Regression Bibliography Koenker and Hullock (2001), “Quantile Regression,” Journal of Economic Perspectives, Vol. 15, Pps. 143-156. Buchinsky (1998), “Recent Advances in Quantile Regression Models”, Journal of Human Resources, Vo. 33, Pps. 88-126.

43. Quantile Regression S+ Programs - Lib.stat.cmu.edu/s www.econ.uiuc.edu/~roger http://Lib.stat.cmu.edu/R/CRAN TSP Limdep