similar to: Re: R-help Digest V2 #283

Hi all, I just discovered R a couple of days ago and I must say it rocks. I've been looking for heteroskedasticity tests and couldn't find any, however. Particularly, I've been told in one of my courses on econometrics of White's method (>< white.test()). The test's statistic is beta / sqrt(W), where W is Var(beta) "? la White", that is the beta(i) matrix is

plot(rnorm(100000),type="l") plots only about 7e4 lines while the same without type="l" works fine. Is this a feature or a bug or is this configurable? R : Copyright 2000, The R Development Core Team Version 1.1.1 (August 15, 2000) SunOS 5.5.1 Generic_103640-29 sun4u sparc SUNW,Ultra-1 Thanks Adrian -- Adrian Trapletti, Olsen & Associates Ltd., See- feldstrasse

Dear colleagues, the tseries package is updated and should now work with R-1.2.0. best and merry Xmas Adrian -- Adrian Trapletti, Olsen & Associates Ltd., See- feldstrasse 233, CH-8008 Zürich, Switzerland Phone: +41 (1) 386 48 48 Fax: +41 (1) 422 22 82 E-mail: adrian@olsen.ch WWW: http://www.olsen.ch

R-help Digest wrote: > Date: Thu, 12 Oct 2000 20:48:59 -0700 (PDT) > From: Elliot Williams <ewilliams at ucsd.edu> > Subject: [R] GARCH in package tseries > > I was running some likelihood ratio tests (using the current version of > tseries) and found a different value for the log-likelihood from what I > was getting using other software. I've traced the problem to

R-help Digest wrote: > > Date: Tue, 28 Nov 2000 08:20:43 +0100 > From: "Muhammad Rashid Ahmed" <rahmed at julian.uwo.ca> > Subject: [R] Fitting of Garch Model in R [forwarded] > > This accidentally (;-) didn't go to the R-help mailing list .. > > - ---- > -- start of forwarded message ------- > > To: <maechler at stat.math.ethz.ch> >

Hi I am actually running the White test for correcting Heteroscedasticity. I used sandwich() & car(), however the output shows the updated t test of coefficients, with revised Standard Errors, however the estimates remained same. My problem is that the residuals formed a pattern in the original regression equation. After running the White's test, I got some new standard errors - but

Hi all, It seems that I can get White's (HC3) test using MASS. The syntax I used for the particular problem is anova(scireg3, white.adjust="hc3") where scireg3 is an object from the lm function. But, the anova summary table is all I get. I don't get the new estimates or standard errors correcting for heteroskedasticity. Is there a way to get that information? Thanks

Hi, I'm a bit confused about what the '--copy-unsafe-links' option considers the 'source tree'. In the man page it says that for links pointing outside the 'source tree' the file will be copied, but when I try this, for all links pointing outside of the local directory the files are copied even though the links point to files that are in the directory tree that is

Hello everyone, I'm working on a current linear model Y = a0 + a1* X1 + ... + a7*X7 + residuals. And I know that this model presents both heteroskedasticity (tried Breusch-Pagan test and White test) and residuals autocorrelation (using Durbin Watson test). Ultimately, this model being meant to be used for predictions, I would like to be able to remove this heteroskedasticity and residuals

Hi- I want to fit a model with crossed random effects and heteroskedastic level-1 errors where inferences about fixed effects are of primary interest. The dimension of the random effects is making the model computationally prohibitive using lme() where I could model the heteroskedasticity with the "weights" argument. I am aware that the weights argument to lmer() cannot be used to

Hello guys! Sorry to bother with such a question I was trying to generate a monte carlo simulation with heteroskedasticity errors. but I am not sure if the command line that I had wrote is quite correct. the type of heteroskedasticity that I want to create is such as var(e) = var(x^4) I began my work with this x<- rnorm (100, 2,0.4) # generating an indepedent random variable e<-

Hello all, I'm hoping someone can help clarify how the survey design method works in R. I currently have a data set that utilized a complex survey design. The only thing is that only the weight is provided. Thus, I constructed my survey design as: svdes<-svydesign(id=~1, weights=~weightvar, data=dataset) Then, I want to run an OLS model, so: fitsurv<-svyglm(y~x1+x2+x3...xk,

I'm new to R and some what new to the world of stats. I got frustrated with excel and found R. Enough of that already. I'm trying to test and correct for Heteroskedasticity I have data in a csv file that I load and store in a dataframe. > ds <- read.csv("book2.csv") > df <- data.frame(ds) I then preform a OLS regression: > lmfit <- lm(df$y~df$x) To

Hello there, I have a linear regression model for which I estimated heteroskedasticity-robust (Huber-White) standard errors using the coeftest function in the lmtest-package. Now I would like to inspect the predicted values of the dependent variable for particular groups and include a confidence interval for this prediction. My question: is it possible to estimate confidence intervals for the

Hey I am trying to run /coeftest()/ using a heteroskedasticity-consistent estimation of the covariance matrix and i get this error: # packages >library(lmtest) >library(sandwich) #test > coeftest(*GSm_inc.pool*, vcov = vcovHC(*GSm_inc.pool*, method="arellano", > type="HC3")) /Fehler in 1 - diaghat : nicht-numerisches Argument f?r bin?ren Operator/ something like:

Folks, Is there any implementation available in R for the simultaneous selection of lag order and rank of a nonstationary VAR as described in Chao & Phillips (1999): Model selection in partially nonstationary vector autoregressive processes with reduced rank structure, J. Econ. (91). Or any other systematic procedure for the consistent selection of lag order and cointegration rank? I

Dear useRs, The package vars, implementing multivariate time series models VAR and VECM, has been updated to version 1.4.7 The new changes are: -the compatibility with the sandwich/lmtest package, which allows to use heteroskedasticity consistent (HC) covariance estimators, to do inference on the parameters taking into account heteroskedasticity of unknown form. -Implementation of a

Dear useRs, The package vars, implementing multivariate time series models VAR and VECM, has been updated to version 1.4.7 The new changes are: -the compatibility with the sandwich/lmtest package, which allows to use heteroskedasticity consistent (HC) covariance estimators, to do inference on the parameters taking into account heteroskedasticity of unknown form. -Implementation of a

Hello Is there any function to calculate White's standard errors in R in an NLS regression. The sandwich and car package do it but they need an lm object to calculate the error's. Does anyone have idea how to do it for an NLS object ? Regards The woods are lovely, dark and deep But I have promises to keep And miles before I go to sleep And miles before I go to sleep ----- [[alternative

Guillermo, I am dropping most of your mail because my answer is very generic. First, why doesn't it work as you tried it: technically speaking, coeftest() and the like expect to be feed an lm or a glm object and for this reason won't accept the result of systemfit(), which is a much different object. I suppose the same goes for the rest. Second, what can you do: I'd do at least one