similar to: Heteroskedasticity and Autocorrelation in SemiPar package

Hello -- I posted this question yesterday and for some reason the post seems to be attached to the wrong thread. Also, I extended my test a little and it seems to indicate the problem is with spm. I would appreciate any help. Thanks. ========================================================== library(plyr) library(SemiPar) data <-

I'm taking the residual-regression approach to semiparametric estimation (Robinson 1988, Econometrica), and basically using SemiPar simply as a convenient means of doing multivariate nonparamteric additive models. The final bit of code is here: finalfit <- spm(res~f(V3,basis="trunc.poly")+f(V5,basis="trunc.poly")+f(V6,basis="trunc.poly")) summary(finalfit)

Hello, I would like to estimate a VAR model with HAC corrected standard errors. I tried to do this by using the sandwich package, for example: > library(vars) > data(Canada) > myvar = VAR(Canada, p = 2, type = "const") > coeftest(myvar, vcov = vcovHAC) Error in umat - res : non-conformable arrays Which suggests that this function is not compatible with the VAR command.

Dear maintainers and R-devel, Several orphaned CRAN packages are about to be archived due to outstanding QC problems, but have CRAN and BioC packages depending on them which would be broken by the archival (and hence need archiving alongside). Therefore we are looking for new maintainers taking over maintainership for one or more of the following packages: R2HTML SemiPar cghseg hexbin lgtdl

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, When you use the default knot choice in spm() (library(SemiPar)) a figure showing the knot locations is sent to the screen and you have to accept the knots to move on. I am trying to run simulations using this function. Is there a way to get spm() to use the default "REML" knots without needing to approve each set of knots? Here is an example: library(SemiPar) data(scallop)

Hi, I have a simple linear regression for which I want to obtain HAC corrected standard errors, since I have significant serial/auto correlation in my residuals, and also potential heteroskedasticity. Would anyone be able to direct me to the function that implements this in R? It's a basic question and I'm sure I'm missing something obvious here. I looked up this post:

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

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

Hello, One of my users has a mailbox named 'INBOX.Kron & SPM' (maybe created a long time ago, when we use courier imap as pop/imap server, but I'm not sure). I can see the maildir with a doveadm list command: amateo_adm at myotis31:~$ sudo doveadm mailbox list -u <user> ... INBOX.Kron & SPM ... but I can't delete it, neithe rename it: amateo_adm at

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 everyone: I want to fit the truncated polynomial smoothing to the quantiles instead of means, does someone know how to do it? I am thinking that maybe I can use SemiPar package, but can not find how. Thanks so many, Suyan

Hello, first of all: I'm new to R and have only used SPSS befor this (which can't do this at all...). I'm trying to output some regression results to latex. The regressions are normal OLS and I'm trying to output the results with standardized regression coefficients and t-statistics based on "Huber-White sandwich estimator for variance". The final result should be

dear R experts: I wonder whether it is possible to extend the summary method for the lm function, so that it uses an option "hccm" (well, model "hc0"). In my line of work, it is pretty much required in reporting of almost all linear regressions these days, which means that it would be very nice not to have to manually library car, then sqrt the diagonal, and recompute

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, I am trying to plot on a spatial map the punctual measurements of the data located in the file (https://opengeo.metu.edu.tr/test.csv). I'd like to have the dots in some way proportional to the magnitudes of the measurements. I have difficulties with the code given below: The dot sizes do not vary not proportionally when varying the coefficient ("0.725"). It either plots

Hi, I'm using maxlik with functions specified (L, his gradient & hessian). Now I would like determine some robust standard errors of my estimators. So I 'm try to use vcovHC, or hccm or robcov for example but in use one of them with my result of maxlik, I've a the following error message : Erreur dans terms.default(object) : no terms component Is there some attributes

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

Good Afternoon Everyone, I am looking for advice fitting a linear mixed model where the random components do not seem to fit within the model formulae for lmer. The columns of Z are not stratified and have the notional random formula (z1 | 1) + ... + (zk | 1). Context I am fitting a penalised thin plate spline with knots k1 to kn. The basis functions Zk are |x-ki|^3 and the penalty matrix has

Hi, I'm running Centos 5.1 on an Intel Quad 2 Core with four Q6600 processors. Despite Centos 5 supporting multiple cores (SPM), it doesn't seem to work for me: 1) a dmesg shows that only one processor is seen: "SPM alternatives: switching to UP code", etc. 2) file /proc/cpuinfo shows only one processor 3) top with option 1 (to toggle between seeing all processors or an