similar to: heteroskedasticity-robust standard errors

I use by() to generate a summary statistics like so: Lbys <- by(dat[Nidx], dat$LipTest, summary) where Nidx is an index vector with names picking out the columns in the data frame dat. This returns a list of character arrays (see below for str() output) where the columns are named correctly but the rownames are empty strings and the values are strings prepended with the summary

> This is another thing that has always put me off. The > virtual block device driver has the ability to masquerade as > other types of block devices. It actually claims to be an > IDE or SCSI device allocating the appropriate major/minor numbers. > > This seems to be pretty evil and creating interesting failure > conditions for users who load IDE or SCSI modules.

> This is another thing that has always put me off. The > virtual block device driver has the ability to masquerade as > other types of block devices. It actually claims to be an > IDE or SCSI device allocating the appropriate major/minor numbers. > > This seems to be pretty evil and creating interesting failure > conditions for users who load IDE or SCSI modules.

I have a computer that dual boots between Windows and CentOS. Each has it's own hard drive. I suddenly have a problem with Windows, and may need to reinstall. I need it for applications like After Effects, Premiere, and other things that I am currently in the middle of a project with. It seems the system has become usnstable, it's preventing me from continuing to work, and I

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

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

For the archives... In trying to play GSM files in Windows (Windows XP for me, but in general) I found no help on Google, so when I figured it out I thought I would post it here. Q: How do I play GSM Files in Windows? A: Use Quicktime, it supports the GSM audio format directly. Andy Farnsworth farnsaw@stonedoor.com

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<-

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

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,

Hey Guys, i want to do a CAPM-GARCH model. I didn?t find anything posted online. (If there is something - shame on me - i didn?t find it.) My Problem: What is the difference if I let the residuals ?e? follow a garch process ? How do I do my regression analysis now? I began reading about regression analyis with heteroscedasticity, but didn?t get it. So i started programming. First

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:

Hi all, Does anyone know how to report heteroskedasticity and autocorrelation-consistent standard errors when using the "spm" command in SemiPar package? Suppose the original command is sp1<-spm(y~x1+x2+f(x3), random=~1,group=id) Any suggestion would be greatly appreciated. Thanks, Susan [[alternative HTML version deleted]]

I just corrected std.error of my 'model'(Multi Regression). Then how can I get new t and p-values? Isn't there any R command which shows new t and p values? -- View this message in context: http://r.789695.n4.nabble.com/After-heteroskedasticity-correction-how-can-I-get-new-confidential-interval-tp3095643p3095643.html Sent from the R help mailing list archive at Nabble.com.

Dear All, I would like to introduce the above-named new package that is now available on CRAN: https://cran.r-project.org/web/packages/skedastic/index.html The package features numerous 'classical' heteroskedasticity tests (some not previously available in any published R package) as well as one very new test that appeared in the literature only in 2019. Feedback on bugs/issues is most

Dear All, I would like to introduce the above-named new package that is now available on CRAN: https://cran.r-project.org/web/packages/skedastic/index.html The package features numerous 'classical' heteroskedasticity tests (some not previously available in any published R package) as well as one very new test that appeared in the literature only in 2019. Feedback on bugs/issues is most

Hi, I'm dealing with time series. I usually use stl() to estimate trend, stagionality and residuals. I test for normality of residuals using shapiro.test(), but I can't test for autocorrelation and heteroskedasticity. Is there a way to perform Durbin-Watson test and Breusch-Pagan test (or other simalar tests) for time series? I find dwtest() and bptest() in the package lmtest, but it

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

Dear all, Trying to test for heteroskedasticity I tried several test from the car package respectively lmtest. Now that they produce rather different results i am somewhat clueless how to deal with it. Here is what I did: 1. I plotted fitted.values vs residuals and somewhat intuitively believe, it isn't really increasing... 2. further I ran the following tests bptest (studentized

The Brown-Forsyth test for homogeneity of variance is included in the HH package, downloadable from CRAN. library(HH) x <- c(rnorm(1000), rnorm(1000, 0, 1.2)) tmp <- data.frame(x=x, group=rep(c("s1","s1.2"), c(1000,1000))) plot.hov(x ~ group, data=tmp) hov(x ~ group, data=tmp)