# Quantile Regression (home made, part 2)

A few months ago, I posted a note with some home made codes for quantile regression… there was something odd on the output, but it was because there was a (small) mathematical problem in my equation. So since I should teach those tomorrow, let me fix them.

## Median

Consider a sample $\{y_1,\cdots,y_n\}$. To compute the median, solve$$\min_\mu \left\lbrace\sum_{i=1}^n|y_i-\mu|\right\rbrace$$which can be solved using linear programming techniques. More precisely, this problem is equivalent to$$\min_{\mu,\mathbf{a},\mathbf{b}}\left\lbrace\sum_{i=1}^na_i+b_i\right\rbrace$$with $a_i,b_i\geq 0$ and $y_i-\mu=a_i-b_i$, $\forall i=1,\cdots,n$. Heuristically, the idea is to write $y_i=\mu+\varepsilon_i$, and then define $a_i$‘s and $b_i$‘s so that $\varepsilon_i=a_i-b_i$ and $|\varepsilon_i|=a_i+b_i$, i.e. $$a_i=(\varepsilon_i)_+=\max\lbrace0,\varepsilon_i\rbrace=|\varepsilon|\cdot\boldsymbol{1}_{\varepsilon_i>0}$$and$$b_i=(-\varepsilon_i)_+=\max\lbrace0,-\varepsilon_i\rbrace=|\varepsilon|\cdot\boldsymbol{1}_{\varepsilon_i<0}$$denote respectively the positive and the negative parts.

Unfortunately (that was the error in my previous post), the expression of linear programs is$$\min_{\mathbf{z}}\left\lbrace\boldsymbol{c}^\top\mathbf{z}\right\rbrace\text{ s.t. }\boldsymbol{A}\mathbf{z}=\boldsymbol{b},\mathbf{z}\geq\boldsymbol{0}$$In the equation above, with the $a_i$‘s and $b_i$‘s, we’re not far away. Except that we have $\mu\in\mathbb{R}$, while it should be positive. So similarly, set $\mu=\mu^+-\mu^-$ where $\mu^+=(\mu)_+$ and $\mu^-=(-\mu)_+$.

Thus, let$$\mathbf{z}=\big(\mu^+;\mu^-;\boldsymbol{a},\boldsymbol{b}\big)^\top\in\mathbb{R}_+^{2n+2}$$and then write the constraint as $\boldsymbol{A}\mathbf{z}=\boldsymbol{b}$ with $\boldsymbol{b}=\boldsymbol{y}$ and $$\boldsymbol{A}=\big[\boldsymbol{1}_n;-\boldsymbol{1}_n;\mathbb{I}_n;-\mathbb{I}_n\big]$$And for the objective function$$\boldsymbol{c}=\big(\boldsymbol{0},\boldsymbol{1}_n,-\boldsymbol{1}_n\big)^\top\in\mathbb{R}_+^{2n+2}$$

To illustrate, consider a sample from a lognormal distribution,

n = 101 set.seed(1) y = rlnorm(n) median(y) [1] 1.077415

For the optimization problem, use the matrix form, with $3n$ constraints, and $2n+1$ parameters,

So far so good…

## Quantile Regression

Consider the following dataset, with rents of flat, in a major German city, as function of the surface, the year of construction, etc.

base=read.table("http://freakonometrics.free.fr/rent98_00.txt",header=TRUE)

The linear program for the quantile regression is now$$\min_{\boldsymbol{\beta}^+,\boldsymbol{\beta}^-,\mathbf{a},\mathbf{b}}\left\lbrace\sum_{i=1}^n\tau a_i+(1-\tau)b_i\right\rbrace$$with $a_i,b_i\geq 0$ and $$y_i=\boldsymbol{x}^\top[\boldsymbol{\beta}^+-\boldsymbol{\beta}^-]+a_i-b_i$$$\forall i=1,\cdots,n$ and $\beta_j^+,\beta_j^-\geq 0$ $\forall j=0,\cdots,k$. So use here

require(lpSolve) tau = .3 n=nrow(base) X = cbind( 1, base$area) y = base$rent_euro K = ncol(X) N = nrow(X) A = cbind(X,-X,diag(N),-diag(N)) c = c(rep(0,2*ncol(X)),tau*rep(1,N),(1-tau)*rep(1,N)) b = base$rent_euro const_type = rep("=",N) r = lp("min",c,A,const_type,b) beta = r$sol[1:K] - r$sol[(1:K+K)] beta [1] 148.946864 3.289674 Of course, we can use R function to fit that model library(quantreg) rq(rent_euro~area, tau=tau, data=base) Coefficients: (Intercept) area 148.946864 3.289674 Here again, it seems to work quite well. We can use a different probability level, of course, and get a plot plot(base$area,base$rent_euro,xlab=expression(paste("surface (",m^2,")")), ylab="rent (euros/month)",col=rgb(0,0,1,.4),cex=.5) sf=0:250 yr=r$solution[2*n+1]+r$solution[2*n+2]*sf lines(sf,yr,lwd=2,col="blue") tau = .9 r = lp("min",c,A,const_type,b) tail(r$solution,2) [1] 121.815505 7.865536 yr=r$solution[2*n+1]+r$solution[2*n+2]*sf lines(sf,yr,lwd=2,col="blue")

And we can adapt the later to multiple regressions, of course,

X = cbind(1,base$area,base$yearc) K = ncol(X) N = nrow(X) A = cbind(X,-X,diag(N),-diag(N)) c = c(rep(0,2*ncol(X)),tau*rep(1,N),(1-tau)*rep(1,N)) b = base$rent_euro const_type = rep("=",N) r = lp("min",c,A,const_type,b) beta = r$sol[1:K] - r\$sol[(1:K+K)] beta [1] -5542.503252 3.978135 2.887234

to be compared with

library(quantreg) rq(rent_euro~ area + yearc, tau=tau, data=base)   Coefficients: (Intercept) area yearc -5542.503252 3.978135 2.887234   Degrees of freedom: 4571 total; 4568 residual

## 2 thoughts on “Quantile Regression (home made, part 2)”

1. Jorge says:

Nice code! Thank you

2. issam says:

Thank you very much, sir, it really helped me on my journey into quantitative regression.
I have a lot of questions regarding this subject, may I ask them?
Thank you

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