similar to: CAPM-GARCH - Regression analysis with heteroskedasticity

All, I'm attempting to run this: table.CAPM(series[,"Strat.Return",drop=FALSE],series[,"spy.Return",drop=FALSE]) and getting this error Error in as.vector(data[, i]) : subscript out of bounds I've searched around and cannot find a solution to the problem.  I've used this in the past without problem and I'm not sure what I did different this time. Other

Dear R users, I am trying to carry out MLE of the time-varying CAPM using the FKF package. My approach so far has been to try and adapt the example given in the help file found using ?fkf which demonstrates the MLE of an ARMA(2,1) model. When I attempt to run my R code (given below) I get the following error: Error in fkf(a0 = sp$a0, P0 = sp$P0, dt = sp$dt, ct = sp$ct, Tt = sp$Tt, : Some of

Hi This is my function. It serves an HTML page after the calculations. I'm connecting to a MSSQL DB using pyodbc. def CAPM(self,client): r=self.r cds="1590" bm="20559" d1 = [] v1 = [] v2 = [] print"Parsing GET Params" params=client.g[1].split("&") for items in

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

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 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

I am trying to compute the white heteroskedasticity-robust standard errors (also called the Huber standard errors) in a linear model, but I can't seem to find a function to do it. I know that the design library in S+ has something like this (robcov?), but I have not yet seen this library ported to R. Anyone know if there is already a function built into R to do this relatively simple job?

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)

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

Dear list members, I am facing a 3-level model, for which my research hypotheses suggest that the variance of both level-1 and level-2 residuals may be a function of a level-3 variable. To be a bit more clear: I am fitting a longitudinal model for a panel of companies grouped in industries. I suggest that some industry variables may create 'unexpected' shocks at especific points in