similar to: some irritation with heteroskedasticity testing

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 >

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

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

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'm very new to R but have plenty of experience with statistics and other packages like SPSS, SAS etc. I have a dataset of around 20 columns and 200 rows. I'm trying to fit a very simple linear model between two variables. Having done so, I want to test the model for heteroscedasticity using the Breusch-Pagan test. Apparently this is easy in R by simply doing bptest(modelCH,

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,

On Mon, 4 Apr 2016, varin sacha via R-help wrote: > Hi Deepak, > > In econometrics there is another test very often used : the white test. > The white test is based on the comparison of the estimated variances of > residuals when the model is estimated by OLS under the assumption of > homoscedasticity and when the model is estimated by OLS under the > assumption of

Respected Sir, I am doing a project on multiple linear model fitting and in that project I have to test Homoscedesticity of errors I have google for the same and found bptest for the same but in R version 3.2.4 bp test is not available. So please suggest me a test on homoscedesticity ASAP as we have to submit our report on 7-04-2016. P.S. : I have plotted residuals against fitted values and it is

Using ordered probit model, I get errors from dwt and bptest. dwt: Error in durbinWatsonTest.default(...) : requires vector of residuals bptest: Error in storage.mode(y) <- "double" : invalid to change the storage mode of a factor I imagine I have to restate as an individual probit model for each category, but is there an easier way? thanks, bp [[alternative HTML version

Hi Deepak, In econometrics there is another test very often used : the white test. The white test is based on the comparison of the estimated variances of residuals when the model is estimated by OLS under the assumption of homoscedasticity and when the model is estimated by OLS under the assumption of heteroscedastic. The White test with R install.packages("bstats") library(bstats)

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Hi, R core team I am using the function {aidsEst} in package [micEcon] to do an AIDS model now. So far, everything is good. But I want to test the auto correlation and heteroskedasticity of the individual equation from AIDS demand system. How can I return the individual equation? PS: serial correlation test is {bgtest} in package [lmtest] and heteroskedasticity is {bptest} in package

Hello, Is there a way to perform a White test (testing heteroscedasticity) under R? Best regards, Afrae Hassouni

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

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,

Hi, Sorry for the question, I know it should be basic knowledge but I'm struggling for two hours now. How do I select only the first entry of each list member and ignore the rest? So for > $"121_at" > -113691170 > $"1255_g_at" > 42231151 > $"1316_at" > 35472685 35472588 > $"1320_at" > -88003869

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 R-programmers, I can't find find the White test to check the homoscedasticity of the residuals from a linear model. Could you please help me with this? Thank you ! BC [[alternative HTML version deleted]]

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

Hi,   I'm actually I’m performing a TSLS linear multiple regression on annually data which go from 1971 to 1997. After performing the TSLS regression, I tried to extract the R squared value using “output$r.squared” function and to perform autocorrelation (Durbin Watson and Breush Godfrey) and heterokedasticity tests (Breush-pagan and Goldfeld Quandt)  but I have errors messages. More