Displaying 20 results from an estimated 110 matches similar to: "Error using coeftest() with a heteroskedasticity-consistent estimation of the covar."
2011 Nov 21
1
Problems using log() in a plm() regression.
hey guys
I have a panel data set that i want to perform some regressions on. I am
using the /plm/ package.
I defined a model in the following way:
PWBw.pool <- plm(*PWB* ~ log(*I_EQON*) + log(*RD*) + ... + *PAGRI*,
data = pfem, na.action=na.exclude, model="pooling")
When i run this it gives the following error (the error remains when i use
other model = "" specifications
2011 Nov 23
0
R: Problems using log() in a plm() regression.
Hello. Just a quick follow-up, for other 'plm' users on the list:
- the problem turned out to be logs of zero values hidden in the big dataset
- trying log(xx) would not reveal the problem, because log(0)=-Inf is a valid result in log() while it is an invalid input to plm()
--> it is always advisable to try lm(yourformula, yourdata) as a first diagnostic check when plm(yourformula,
2009 Dec 10
2
Problem with coeftest using Newey West estimator
Hi,
I want to calculate the t- and p-values for a linear model using the Newey West estimator.
I tried this Code and it usually worked just fine:
> oberlm <- lm(DYH ~ BIP + Infl + EOil, data=HU_H)
> coeftest(oberlm, NeweyWest(oberlm, lag=2))
t test of coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1509950 0.0743832 2.0300 0.179486
BIP
2010 May 11
2
ANCOVA in R, single CoVar, two Variables
Hello,
I am VERY new to R, just picking it up infact. I have got my head around the
basics of ANOVA with post hoc tests but I am struggling with regression,
especially with ANCOVAs.
I have two sets of data, one of type A, one of type B. Both have been placed
in a wind tunnel and sampled every week. The co variate is of course the
days since the start.
An example is
day A B
0 10.0 10.0
7 9.0
2006 Feb 22
1
var-covar matrices comparison
> Date: Mon, 20 Feb 2006 16:43:55 -0600
> From: Aldi Kraja <aldi at wustl.edu>
>
> Hi,
> Using package gclus in R, I have created some graphs that show the
> trends within subgroups of data and correlations among 9 variables (v1-v9).
> Being interested for more details on these data I have produced also the
> var-covar matrices.
> Question: From a pair of two
2011 Jan 31
2
computing var-covar matrix with much missing data
Is there an R function for computing a variance-covariance matrix that
guarantees that it will have no negative eigenvalues? In my case, there
is a *lot* of missing data, especially for a subset of variables. I think
my tactic will be to compute cor(x, use="pairwise.complete.obs") and then
pre- and post-multiply by a diagonal matrix of standard deviations that
were computed based
2006 Feb 20
1
var-covar matrices comparison:
Hi,
Using package gclus in R, I have created some graphs that show the
trends within subgroups of data and correlations among 9 variables (v1-v9).
Being interested for more details on these data I have produced also the
var-covar matrices.
Question: From a pair of two subsets of data (with 9 variables each, I
have two var-covar matrices for each subgroup, that differ for a
treatment on one
2011 Feb 16
2
covar
Hi all,
I want to construct relatedness among individuals and have a look at the
following script.
#########################
rm(list=ls())
N=5
id = c(1:N)
dad = c(0,0,0,3,3)
mom = c(0,0,2,1,1)
sex = c(2,2,1,2,2) # 1= M and 2=F
A=diag(nrow = N)
for(i in 1:N) {
for(j in i:N) {
ss = dad[j]
dd = mom[j]
sx = sex[j]
if( ss > 0
2010 Dec 27
0
Heteroskedasticity and autocorrelation of residuals
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
2011 Nov 24
1
CAPM-GARCH - Regression analysis with heteroskedasticity
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
2008 Apr 26
0
Help with simulation of heteroskedasticity
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<-
2006 Sep 29
0
Heteroskedasticity test
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)
2009 Jun 26
1
Heteroskedasticity and Autocorrelation in SemiPar package
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]]
2011 Jul 25
1
predict() and heteroskedasticity-robust standard errors
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
2000 Dec 07
2
Heteroskedasticity in R
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
2010 Dec 20
1
After heteroskedasticity correction, how can I get new confidential interval?
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.
2012 Apr 15
0
correct standard errors (heteroskedasticity) using survey design
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,
2013 Apr 05
1
white heteroskedasticity standard errors NLS
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
2020 Jan 13
0
Introducing skedastic: Heteroskedasticity Diagnostics for Linear Regression Models
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
2020 Jan 13
0
Introducing skedastic: Heteroskedasticity Diagnostics for Linear Regression Models
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