similar to: regression and dw

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 want to estimate the following fixed effect model: y_i,t = alpha_i + beta_1 x1_t + beta_2 x2_i,tx2_i,t = gamma_i + gamma_1 x1_t + gamma_2 Z1_i + gamma_3 Z2_i I can use ivreg from AER to do the iv regression. fm <- ivreg(y_i,t ~ x1_t + x2_i,t | x1_t + Z1_i + Z2_i, data = DataSet) But, I'm not sure how can I add the fixed effects. Thanks! [[alternative HTML

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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

Hello Uwe First, I want to thank you for spending your time replying to my mail. I'm very impressed with the speed that my question was answered. I'm new at R (about two weeks) and reading your mail made me realize that it was indeed a question of vectors of different lengths. I thinked that I could create a function ("carfun") without creating a "x" vector, since

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")

Is it possible to fit a lagged regression, "y[t]=b0+b1*x[t-1]+e", using the function "lag"? If so, how? If not, of what use is the function "lag"? I get the same answer from y~x as y~lag(x), whether using lm or arima. I found it using y~c(NA, x[-length(x)])). Consider the following: > set.seed(1) > x <- rep(c(rep(0, 4), 9), len=9) > y <-

Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only

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?

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 fitting a nonlinear mixed-effects model from a balanced panel of data using nlme. I would like to know whay would be the best options for formally testing for autocorrelation. Is it possible to carry out a Durbin-Watson test on a nlme object? As far as I've seen, I think the durbin.watson function from the car package just works on lm objects. Thank you very much, Antonio

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

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

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

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 there. Just a little status update how that integerization is coming along. I am trying to limit myself to 32 bit arithmetics. That means not using any __int64 or long long datatypes at any point. I have now replaced all steps up to including the estimation of the LPC filter coefficients with integer code. That is about a quarter of the total work completed, I would say. One problem that i

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in

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

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

At Mon, 19 Nov 2001 08:36:38, you wrote: > I get the labels I expect: if this is quarterly data the lags are labelled > in years. That is what `frequency = 4' is intended to mean: 4 > observations per unit of time. some further thoughts convinces me that this is a mis-feature. if you ask any person what is the lag i autocorrelation, the answer would be corr(y_t, y_{t-i}). so you