version 1.3.0.0 (3.94 KB) by
Aslak Grinsted

Quantile regression with bootstrapping confidence intervals

Quantile Regression

USAGE: [p,stats]=quantreg(x,y,tau[,order,nboot]);

INPUTS:

x,y: data that is fitted. (x and y should be columns)

Note: that if x is a matrix with several columns then multiple

linear regression is used and the "order" argument is not used.

tau: quantile used in regression.

order: polynomial order. (default=1)

nboot: number of bootstrap surrogates used in statistical inference.(default=200)

stats is a structure with the following fields:

.pse: standard error on p. (not independent)

.pboot: the bootstrapped polynomial coefficients.

.yfitci: 95% confidence interval on polyval(p,x)

Note: uses bootstrap on residuals for statistical inference. (see help bootstrp)

check also: http://www.econ.uiuc.edu/~roger/research/intro/rq.pdf

EXAMPLE:

x=(1:1000)';

y=randn(size(x)).*(1+x/300)+(x/300).^2;

[p,stats]=quantreg(x,y,.9,2);

plot(x,y,x,polyval(p,x),x,stats.yfitci,'k:')

legend('data','2nd order 90th percentile fit','95% confidence interval','location','best')

For references on the method check e.g. and refs therein:

http://www.econ.uiuc.edu/~roger/research/rq/QRJEP.pdf

Copyright (C) 2008, Aslak Grinsted

Aslak Grinsted (2020). quantreg(x,y,tau,order,Nboot) (https://www.mathworks.com/matlabcentral/fileexchange/32115-quantreg-x-y-tau-order-nboot), MATLAB Central File Exchange. Retrieved .

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Fankun BuHi Aslak,

Thanks for the code.

However, I got the message like below. Do you know the reason?

"Exiting: Maximum number of function evaluations has been exceeded

- increase MaxFunEvals option.

Current function value: 334661.090416 "

David KaftanI also found that Koenker - referenced here - translated his R code to matlab at one point. the link to that code is on this page: http://www.econ.uiuc.edu/~roger/research/rq/rq.html

David KaftanThe code is well written, but you should not optimize using fminsearch. Quantile regression can be framed as a linear programming problem, guaranteeing finding the optimum solution and doing so in little time. I recommend the R package quantreg or - if you need matlab - this stackoverflow post. https://stackoverflow.com/questions/21657158/quantile-regression-with-linprog-in-matlab

Dehua ShenDehua Shenzhichao shi@Antony: I have the same question with you for a big dataset. But the results seem to be right.

Christian StanoHi Aslak, I am inexperienced using quantile regression and was wondering if you or anyone could break down what the different numbers in the p statistic mean.

Yao RuiQihi,Aslak.I have some questions to ask you.Now I want to use this code to estimate the parameters of model.For example.x1=a*x2+b*x3+c*x4+d*x2.^2+e*x3.^2+f*x4.^2...

Xiaoyang DongCorrection: Not coefficient estimates. I mean standard errors.

Xiaoyang DongHi Aslak, thanks for your contribution. I'm wondering why when I switch the order of [x1,x2] to [x2,x1], the coefficients estimates lose the rotation invariance property?

Zhongfang HeHi Aslak, first thank you very much for sharing the code.

A quick question is about the way the standard errors of the parameter estimates are bootstrapped in your code. For many cross-sectional data, it is not unreasonable to assume independence btw data points where it is fine to use the bootstrap method in your code. However, if one is dealing with time series data, there is usually serial correlation btw data points, a more suitable bootstrap method should be used, e.g. the stationary bootstrap of Politis&Romano(1994). I would suggest that the independence assumption is explicit in the code description and perhaps have another code specifically for correlated data points. Best regards, Zhongfang

Aslak Grinsted@lisa: they are the polynomial coefficients. They can be interpreted similar to the ouput from polyval

LisaHi, very helpful code, thank you.

A quick question: p outputs several values depending on the order, what do they represent?

thanks!

Aslak Grinsted@SimeonYurek: thank you for the suggestion. It has been implemented.

weihi. just a quick question. How do we calculate the goodness of fit for quantile regression? thanks

Simeon YurekVery nice code for Koenker and Hallock (2001). Thanks for posting.

One question: in your statement of the function rho (line 85), when r >= 0 (all positive residuals above x*p), does the function reduce to abs(r), and thus is not weighted by tau? It's true that [r - 0.*r/tau] = r.*tau, but is the tau lost (being multiplied by zero)? Could you have stated instead:

rho=@(r)sum(abs(r).*abs(tau-(r<0)))

and this way weight both over and under residuals? The stats for the bootstrap are slightly less robust but not by much. Please let me know if I'm off.

Antony: help fminsearch. 'MaxFunEvals' and 'MaxIter' can be defined as options.

AntonyHi, first of all, thank you very much for the code.

When I run the code I get the following message:

"Exiting: Maximum number of function evaluations has been exceeded

- increase MaxFunEvals option.

Current function value: 3183.464509 "

I get this message a number of times with just the current function value changing.

While it is not an error message and I still obtain the estimations I need, I was wondering if this had any influence on the validity of my results.

Thank you in advance for anyone who can help me out here.

Wolfgang SchwanghartExcellent. Well written help and code! Runs as advertised!

SatisIf X and y have some missing observations, then

[p,stats]=quantreg(X,y,.9); does not work. I got error message.

Could you please suggest me how to resolve this problem??

Aslak Grinsted@mohammad: The code does do MLR. Here's an example with multiple predictors:

X=randn(100,4);

y=X*[4 2 4 1]'+randn(100,1)*.2;

[p,stats]=quantreg(X,y,.9);

NikiIn fact I wanted to run this M-file for multi column X, but I got error, so it is not implement for MLR? if it is , could you please put an example? because when I do not mention Order , I get error when i mention it is not MLR

Aslak GrinstedThanks for the comment, and suggestions for improvement. I've just uploaded a new version (it should be online shortly).

ASOne unexpected thing with this code. Suppose I have a Y vector and want to regress it on one explanatory variable, but also include a constant in my regression. Then my X matrix has 2 columns, the first column is just ones, the second is the explanatory variable. It should still be possible to plot this along with the results from the regression.

Currently, however, if you run:

quantreg([ones(length(x),1) x], y,.5)

You get an error because it tries to plot it, but order has been set (line 44) to [], so things get messed up.

Additionally, the plots that do get produced look odd because the default is to draw lines between all of the points, which usually isn't what you want. For example, this doesn't look like what it should:

x=randn(1000,1);

y=1+5*x+randn(1000,1);

quantreg(x,y,0.5)

Finally, there's a problem with the error checking of inputs (lines 41-47) because it's all one big if-else statement. If you only put in three inputs, then line 44 runs and order gets set to [], but then the program exits the if-else statement and so Nboot never gets set correctly. You should split these up into separate if statements because if there are only 3 inputs then you need to set both order and Nboot.