similar to: Heteroskedasticity and autocorrelation of residuals

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

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

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]]

Hi all, I'm getting contradictory results from bptest and ncvTest on a model calculated by GLS as: olslm = lm(log(rr)~log(aloi)*reg*inv, data) varlm = lm(I(residuals(olslm)^2)~log(aloi)*reg*inv, data) glslm = lm(log(rr)~log(aloi)*reg*inv, data, weights=1/fitted(varlm)) Testing both olslm and glslm with both ncvTest and bptest gives: > ncvTest(olslm) Non-constant Variance Score Test

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

First I have to say I am sorry because I have not been so clear in my previous e-mails. I will try to explain clearer what it is my problem. I have the following model: lnP=Sc+Ag+Ag2+Var+R+D In this model the variable Sc is endogenous and the rest are all objective exogenous variables. I verified that Sc is endogenous through a standard Hausman test. To determine this I defined before a new

Dear R users, I have a question here library(AER) library(plm) library(sandwich) ## take the following data data("Gasoline", package="plm") Gasoline$f.year=as.factor(Gasoline$year) Now I run the following regression rhs <- "-1 + f.year + lincomep+lrpmg+lcarpcap" m1<- lm(as.formula(paste("lgaspcar ~", rhs)), data=Gasoline) ###Now I want to find the

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 again Max. A correction to my response from yesterday. Things were better than they seemed. I thought it over, checked Arellano's panel book and Driscoll and Kraay (Rev. Econ. Stud. 1998) and finally realized that vcovSCC does what you want: in fact, despite being born primarily for dealing with cross-sectional correlation, 'SCC' standard errors are robust to "both

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:

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

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Dear useRs, here is the announcement for the next "new" package: sandwich 0.1-3. sandwich provides heteroskedasticity (and autocorrelation) consistent covariance matrix estimators (also called HC and HAC estimators). The former are implemented in the function vcovHC() (which was available in strucchange before - and independently in hccm() in John Fox's car package). And the

Dear useRs, here is the announcement for the next "new" package: sandwich 0.1-3. sandwich provides heteroskedasticity (and autocorrelation) consistent covariance matrix estimators (also called HC and HAC estimators). The former are implemented in the function vcovHC() (which was available in strucchange before - and independently in hccm() in John Fox's car package). And the

Hi, I am trying to obtain the heteroskedasticity consitent standard errors (HCSE) after apply 2SLS. I obtain 2SLS through tsls from package sem or systemfit: #### tsls #### library (sem) Reg2SLS <-tsls(LnP~Sc+Ag+Ag2+Var+R+D,~I2+Ag+Ag2+Var+R+D) summary (Reg2SLS) #### systemfit #### library (systemfit) RS <- LnP~Sc+Ag+Ag2+Var+R+D Inst <- ~I2+Ag+Ag2+Var+R+D labels

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,

Ok I made Jarque-Bera test to the residuals (merv.reg$residual) library(tseries) jarque.bera.test(merv.reg$residual) X-squared = 1772.369, df = 2, p-value = < 2.2e-16 And I reject the null hypotesis (H0: merv.reg$residual are normally distributed) So I know that: 1 - merv.reg$residual aren't independently distributed (Box-Ljung test) 2 - merv.reg$residual aren't indentically

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

data:http://fmwww.bc.edu/ec-p/data/wooldridge/CRIME4.dta > a$call lm(formula = clcrmrte ~ factor(year) + clprbarr + clprbcon + clprbpri + clavgsen + clpolpc, data = cri) > bptest(a,st=F) Breusch-Pagan test data: a BP = 34.4936, df = 10, p-value = 0.0001523 > bptest(a,st=T) studentized Breusch-Pagan test data: a BP = 10.9297, df = 10, p-value = 0.363 >