similar to: Quantile Regression for longitudinal data

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R While this code run perfectly, it does not work for my data providing a warning message: In rq.fit.sfn(D, y, rhs = a) : tiny

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R This code run perfectly. Then I want to know what the result means.The result show $ierr,$it,and $time. What these estimators

Quantile Regression for Longitudinal Data. Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R I am trying to change the number quantiles being estimated. I change the codes about

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

All - Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Specifically, I'm wondering if anyone has implemented the goodness-of-fit process developed by Koenker and Machado (1999) for R? Though I have used package quantreg in the past, I may have overlooked this function, if it is included. Citation: Koenker, R. and

Dear R, I have a question concerning quantile regression models. I am using the quantile regression model to test the relationship between abalone and the percentage cover of algae etc at different sites and depths. An example is fit<-rq(abalone~%coversessileinvertebrates+factor(Depth)+factor(Site),tau=0.7) In this model depth is fixed and site is random. My question is, is it possible

Dear Dimitris, I have exactly the same problem than you, Do you get some solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo de

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of

I am relatively new to R, but am intrigued by its flexibility. I am interested in quantile regression and quantile estimation as regards to cotton fiber length distributions. The length distribution affects spinning and weaving properties, so it is desirable to select for certain distribution types. The AFIS fiber testing machinery outputs a vector for each sample of type c(12, 235, 355, . . .

Hi, Can one tell me which procedure will fit an ordinal logistic regression model for longitudinal data set. To be precise, I have both dichotomous and polytomous items. Also, I would like to specify different covariance structures (unstructured, ar1 etc) for trial runs. Thanks -- View this message in context:

Apologies, forgot to copy R-help on this response. Begin forwarded message: From: Roger Koenker <rkoenker at illinois.edu> Subject: Re: [R] effects() extractor for a quantile reqression object: error message Date: September 6, 2024 at 8:38:47?AM GMT+1 To: "Christopher W. Ryan" <cryan at binghamton.edu> Chris, This was intended to emulate the effects component of lm()

Hello Everyone, I'm currently learning about quantile regressions. I've been using an optimizer to compare with the rq() command for quantile regression. When I run the code, the results show that my coefficients are consistent with rq(), but the intercept term can vary by a lot. I don't think my optimizer code is wrong and suspects it has something to do with the starting

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem,

Hi, Can anyone tell me if they know of any functions written for regression trees with a longitudinal dataset. I've got tree, rpart and maptree, but these all seem to be for univariate responses. Thanks for your time, Cath -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

Dear list, I'm using the quantreg package for quantile regression. Although it's fine, there're is some weird behavior a little bit difficult to understant. In some occasions, the regression results table shows coefficients, t-statistics, standard errors and p-values. However, in other occasions it shows only coefficients and confidence intervals. Therefore, the question is... Is

Dear R users, I am trying to estimate a median regression with fixed effects. I have an unbalanced panel data set with 5,000 individuals and 10 years, resulting in a total of 20,000 observations. When I try to add individual (firmid) fixed effects to the quantile regression using the following command: result<-rq(y~x+factor(firmid),tau=0.5) I get the following error message: "Error:

Dear all, I 'm implementing the koenker procedure for quantile fixed effects. I would like also to apply weights to the procedure, so that to give more weight to the observation that better represent my original sample (much larger than it is possible to use in R). Do you know if it is possible? How could I solve this problem? Thank you alessia