similar to: Test for autocorrelation in nlme model

Hi, I am trying to learn time series, and I am attending a colleague's course on Econometrics. However, he uses e-views, and I use R. I am trying to reproduce his examples in R, but I am having problems specifying a AR(1) model. Would anyone help me with my code? Thanks in advance! Reproducible code follows: download.file("https://sites.google.com/a/proxima.adm.br/main/ex_32.csv

Hi, I ran an experiment with 3 factors, 2 levels and 200 replications and as I want to test for residuals independence, I used Durbin-Watson in R. I found two functions (durbin.watson and dwtest) and while both are giving the same rho, the p-values are greatly differ: > durbin.watson(mod1) lag Autocorrelation D-W Statistic p-value 1 -0.04431012 2.088610 0.012 Alternative

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, am trying to understand speex's algo. have a few questions. 1) autocorrelation: in the function, _spx_autocorr (for floating point version), there is a line ac[0] += 10; correct me if i am wrong, i suppose the addition of 10 is used to condition the autocorrelation matrix. wonder how the value of 10 is arrived at? 2) levinson durbin (LD) algo in the function _spx_lpc, i referred

Should "dwtest" and "durbin.watson" be giving me the same DW statistic and p-value for these two fits? library(lmtest) library(car) X <- c(4.8509E-1,8.2667E-2,6.4010E-2,5.1188E-2,3.4492E-2,2.1660E-2, 3.2242E-3,1.8285E-3) Y <- c(2720,1150,1010,790,482,358,78,35) W <- 1/Y^2 fit <- lm(Y ~ X - 1) dwtest(fit,alternative="two.sided")

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

I have been reading about autocorrelation in linear models over the last couple of days, and I have to say the more I read, the more confused I get. Beyond confusion lies enlightenment, so I'm tempted to ask R-Help for guidance. Most authors are mainly worried about autocorrelation in the residuals, but some authors are also worried about autocorrelation within Y and within X vectors

Hi, I have a problem with a regression I try to run. I did an estimation of the market model with daily data. You can see to output below: /> summary(regression_resn) Time series regression with "ts" data: Start = -150, End = -26 Call: dynlm(formula = ror_resn ~ ror_spi_resn) Residuals: Min 1Q Median 3Q Max -0.0255690 -0.0030378 0.0002787

Hi, P-values in Durbin-Watson test obtained through the use of functions available in packages "lmtest" and "car" are different. The difference is quite significant. function "dwtest" in "lmtest" is much faster than "burbinwatson" in "car". Actually, you can take a nap while the latter trying to calculated Durbin-Watson test. My question

I am trying to evaluate a model by using the commands durbin.watson and cr.plot. However, I keep getting errors that I can't figure out. A description follows. Does anyone have a hint as to what may be wrong? 1)The Durbin Watson Test. In running the command I kept getting the message "residuals include missing values" when actually this was NOT the case. Example:

Dear all, I am trying to fit a nonlinear model with a autocorrelation term, but everytime I type in the command, I got an error message from Winwows and R closes itself. The command line is as follows: mod1<-nlme(V~A*exp(-B*A.O)*Vac.t.1.,data,fixed=A+B~1,random=A+B~1|ORDINAL,+ correlation=corCAR1(0.3179,~A.O|ORDINAL,TRUE),start=c(A=1.2,B=0.2)) I have already fitted this model allowing Phi to

Dear all, I am trying to fit a mixed-effects non linear regression, but I have some trouble with it. My data are a balanced panel of 904 subjects with 8 observations (at regular periods) per subject. The functional form of my model is Y=Aexp(-BX1)X2 +e. I want to allow parameters A and B to vary among subjects and also include an autocorrelation term. I have already fitted a standard nonlinear

Hi, I have two time series, y and x. Diff(y) and Diff(x) both show no autocorrelation. But durbin.watson(lm(Diff(y)~lag(Diff(x),k=-4)) gives a DW value of zero. How come the residule is autocorrelated while Diff(y) and Diff(x) are not? Does anyone know if in my case a DW of zero indicates serial correlation, or is it telling me that the DW statistics is not the appropriate statistics to use here?

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

Dear R People: Suppose we have a regression model that we will call y.lm We run the Durbin Watson test for autocorrelation and we find that there is positive autocorrelation, and phi = 0.72, say. What is our next step, please? Do we calculate the following yprime_t = y_t - 0.72y_t-1, x1prime_t = x1_t - 0.72x1_t-1, and so on, and re-fit the linear mode? I haven't done this in a while.

Hi Everyone, I am trying to do a time series regression using the lm function. However, according to the durbin watson test the errors are autocorrelated. And then I tried to use the gls function to accomodate for the autocorrelated errors. My question is how do I know what ARMA process (order) to use in the gls function? Or is there any other way to do the time series regression in R? I highly

Hello all- I have two time series, Index1stdiff and Comps1stdiff. I regressed the first on the second and R returned the summary stats I expected. Then I looked at and plotted the residuals. I then wanted to assess autocorrelation characteristics and tried to run a Durbin-Watson using: library(lmtest) dwtest(formula=Index1stdiff~Comps1stdiff,alternative=c("greater")) I am

Sir,   I am unable to get the source code for Durbin-Watson test, as I want to calculate the p-value for Durbin Watson statistic using interpolation method. I sent this mail to r-help, but it was rejected, please suggest me some way. I will be highly greatful to you. Thanks in advance Ramesh [[alternative HTML version deleted]]

Hello! I have a data frame mysample (sorry for a long way of creating it below - but I need it in this form, and it works). I regress Y onto X1 through X11 - first without weights, then with weights: regtest1<-lm(Y~., data=mysample[-13])) regtest2<-lm(Y~., data=mysample[-13]),weights=mysample$weight) summary(regtest1) summary(regtest2) Then I calculate Durbin-Watson for both regressions

  Dear Arne Henningsen,   I send you this message because I have question with regard to systemfit package. I hope you answer to my request.   I estimated a system of equation bu using SUR method. The function summary(xx) gives me summary of estimated equation system. However, this function does not give my the value of the durbin watson statistic  for each one of my equations (to chek for serial