similar to: Codes; White's heteroscedasticity test and GARCH models

Hi I am actually running the White test for correcting Heteroscedasticity. I used sandwich() & car(), however the output shows the updated t test of coefficients, with revised Standard Errors, however the estimates remained same. My problem is that the residuals formed a pattern in the original regression equation. After running the White's test, I got some new standard errors - but

Hello R helpers, I am trying to do a linear OLS regression of y on two variables x1 and x2. I want to constrain the coefficients of x1 and x2 to sum up to 1. and therefore run a constrained OLS. Can anybody help with this? (I have seen some answers to similar questions but it was not clear to me what I need to do) - I have tried the lm function with offset but I must not have used it properly.

Hello everybody, I've got a matrix called EUROPEDATA and I want to calculate the adf test statistic (part of the tseries package) on a rolling basis for window my.win on each column; i.e. each column of EUROPEDATA represents a particular variable; for the first column I calculate the adf test statistic for window my.win = 60 for example, roll forward one observation, calculate the adf

Dear R helpers, I am trying to read a CSV file in R called EUROPE (originally an Excel file which I have saved as a CSV file) using the command EUROPEDATA <- read.csv("EUROPE.csv") EUROPE.csv is basically a matrix of dimension 440*44, and has a line of headers, i.e. each column has a name. Using read.csv I can't load the data into R properly. Although the first 20 columns or

Hello, I am looking for a code in R for the variance ratio test statistic (the Lo and Mackinlay version or any other versions). Does anybody have such a code they can share or know a library in which I can find this function? Basically I have a number of time series which I need to check for persistence. One other test I can use is the runs test in the tseries package. Any help will be greatly

Dear All, i want to use garch model in return of stock. and the data should presence volatility cluster (Heteroscedasticity). Do you know how to test volatility cluster (the presence of heteroscedasticity) in series data of stock return in R? Is it using Langrange Multiplier (LM) ARCH test? what package i should use? I really need the help. Thanks for the attention. Eko A P

Dear all, Sorry if this is too obvious. I am trying to fit my multiple regression model using lm() Before starting model simplification using step() I checked whether the model presented heteroscedasticity with ncv.test() from the CAR package. It presents it. I want to correct for it, I used hccm() from the CAR package as well and got the Heteroscedasticity-Corrected Covariance Matrix. I am not

Using hccm() I got a heteroscedasticity correction factor on the diagonal of the return matrix, but I don't know how to incorporate this into my linear model: METHOD 1: > OLS1 <- lm(formula=uer92~uer+low2+mlo+spec+degree+hit) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0623377 0.0323461 -1.927 0.057217 . uer 0.2274742 0.0758720

Hello, Could anyone please tell me how to estimate Heteroscedasticity Consistent standard errors for a Spatial error model? All the functions I have looked at only works for lm objects. Thank you very much! - Oshadhi

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

Hi r-programmers, I performe Breusch-Pagan tests (bptest in package lmtest) to check the homoscedasticity of the residuals from a linear model and I carry out carry out White's test via bptest (formula, ~ x * z + I(x^2) + I(z^2)) include all regressors and the squares/cross-products in the auxiliary regression. But what can I do if I want find coefficient and p-values of variables x, z, x*z,

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

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

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

Hi, I am currently trying to do a GLMM on a dataset with percent cover of seagrass (dep. var) and a suite of explanatory variables including algal (AC) and epiphyte cover (EC), rainfall, temperature and sunshine hours. M2=glmer(SG~AC+EC+TP+SS+RF+(1|Location/fSi/fTr), family=binomial,data=data,nAGQ=1) As the dependent variable is percent cover, I used a binomial error structure. I also have a

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

Hi,I try to calcualte AIC or Loglik to GARCH model,But the Packege Tseries do not deal with them.How can I calculate AIC or Loglike to GARCH Model By Packege Tseries? Thanks. ____________________________________________________ Free Internet Access NOW! In Alexandria, Ismaileya, Suez, Portsaid, Hurgadha, Sharm Banha, Shebin El-Kom, Damietta, Tanta, Zagazig, Mansoura, Damanhour, Assyout, Qena

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

Hi, I would like to generate two correlated variables. I found that funktion for doing that: a <- rmvnorm(n=10000,mean=c(20,20),sigma=matrix(c(5,0.8*sqrt(50), 0.8*sqrt(50),10),2,2)) (using library(mvtnorm)) Now I also want to generate two correlated variables where the error variance vary over the variable-correlation. And I want to plot this for showing heteroscedasticity. Like shown

Dear lmer users, The experiment includes 15 groups of (3 males and 1 female). The female is characterized by its quality Q1 and Q2. Each male of a group is characterized by the number of MatingAttempts (with Poisson distribution). I want to examine if male mating attempts depend on female quality. I can see from graphic exploration that the within-group heterogeneity of male attempts increases