similar to: quantile regression - estimation of CAViaR

How is it possible to estimate the conditional autoregressive Value-at-Risk model qantile_t(tau)=a0+a1*qantile_(t-1)(tau)+a2*abs(r_(t-1)) see http://www.faculty.ucr.edu/~taelee/paper/BLSpaper1.pdf (page 10)) of Engle & Manganelli in R? The qantile_(t-1)(tau)-term causes headache. Kind regards, Jaci -- "Ein Herz f?r Kinder" - Ihre Spende hilft! Aktion: www.deutschlandsegelt.de

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Hi, I am trying to replicate Engle and Manganelli's paper Conditional Autoregressive Value at Risk (CAViaR) by Regression Quantiles. I have the Matlab code which I cannot get to work as I have never used Matlab before, does anyone know if there is the same code available to estimate the CAViaR models in R? Thanks, Shane -- View this message in context:

Hi list, I googled around but couldn''t find anything on whether someone has good or bad experiences with the Caviar *Blue* drives? I saw in the archives Caviar Blacks are *not* recommended for ZFS arrays (excluding apparently RE3 and RE4?). Specifically I''m looking to buy Western Digital Caviar Blue WD10EALS 1TB drives [1]. Does anyone have any experience with these drives? If

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 all, I would like to implement a spatial quantile regression using instrumental variable estimation (according to Su and Yang (2007), Instrumental variable quantile estimation of spatial autoregressive models, SMU economics & statistis working paper series, 2007, 05-2007, p.35 ). I am applying the hedonic pricing method on land transactions in Luxembourg. My original data set contains

Hello, is there any function/package in R for multivariate quantile estimation? (I couldn't find any for the moment) Thank you! Anna

Dear friends, I need the get the 2.5% and 97.5% quantile from each row of a matrix, how should i get it? BTW, i can get the min/max value from each row of a matrix, using the following programs, is there an easy function to do it? simmin<-matrix(NA,nrow=47,ncol=1) for (i in 1:47) { simmin[i,]<-min(datas[i,]) } Thanks for your help. -- With Kind Regards, oooO:::::::::

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, . . .

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 trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

Greetings does anyone know of an R package that can do quantile regression with instrumental variables. I've found 'sem' for IV estimation and 'quantreg' for quantile regression but I would like to find something that can do a quantile regression with instrumental variables. Cheers, Neil ============================================= Neil Hepburn, Economics Instructor

I am new to R and am interested in using the program to fit quantile regression models to data collected from a multi-stage probability sample of the US population. The quantile regression package, rq, can accommodate person weights. However, it is not clear to me that boot.rq is appropriate for use with multi-stage samples (i.e., is capable of sampling primary sampling units instead of survey

I am working on a two stage threshold quantile regression model in R, and my aim is to estimate the threshold of the reduced-form equation (call it rhohat), and the threshold of the structural equation (call it qhat), in two stages. On the first stage, i estimate rhohat by quantile regression and obtain the fitted values. I use these fitted values to estimate qhat on the second stage. The code is

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

I know that this has been well-discussed already, but it''s been a few months - WD caviars with mpt/mpt_sas generating lots of retryable read errors, spitting out lots of beloved " Log info 31080000 received for target" messages, and just generally not working right. (SM 836EL1 and 836TQ chassis - though I have several variations on theme depending on date of purchase: 836EL2s,

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

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2

Mike, Do something like: require(rms) dd <- datadist(mydatarame); options(datadist='dd') f <- Rq(y ~ rcs(age,4)*sex, tau=.5) # use rq function in quantreg summary(f) # inter-quartile-range differences in medians of y (b/c tau=.5) plot(Predict(f, age, sex)) # show age effect on median as a continuous variable For more help type ?summary.rms and ?Predict Frank ------------