similar to: Autocorrelated error prediction correction

Hi, I am using 2 times series and I want to carry out a regression of Seri1 by Serie2 using structured (autocorrelated) errors. (Equivalent to the autoreg function in SAS) I found the function gls (package nlme) and I made: gls_mens<-gls(mening_s_des~dataATB, correlation = corAR1()) My problem is that I don’t want a AR(1) structure but ARMA(n,p) but the execution fails :

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

Dear all, I met with some problems when dealing with a time series with serial correlation. FIRST, I generate a series with correlated errors set.seed(1) x=1:50 y=x+arima.sim(n = 50, list(ar = c(0.47))) SECOND, I estimate three constants (a, b and rho) in the model Y=a+b*X+u, where u=rho*u(-1)+eps library(nlme) gls(y~x,correlation = corAR1(0.5)) # Is it the right procedure?

Dear R users, In my data, there are two variables t1 and t2. For each observation of t1 and t2, two location indicators (x, y) were provided. The data format is # x y t1 t2 Since the both t1 and t2 are depended on x and y, t1 and t2 are autocorrelated variables. My question is how to calculate the correlation between t1 and t2 by taking into account the structure of residual variance

Hello folks, I have some data where spatial autocorrelation seems to be a serious problem, and I'm unclear on how to deal with it in R. I've tried to do my homework - read through 'The R Book,' use the online help in R, search the internet, etc. - and I still have some unanswered questions. I'd greatly appreciate any help you could offer. The super-super short explanation is

Hi, I have two sets of data for a given set of (non-lattice) locations. I would like to know whether the two are significantly different. This would be simple enough if it wasn't for the fact that the data is spatially autocorrelated. I have come across several possible solutions (including Cliff & Ord which however appears to be for gridded data), or using gls. However, they don't

How does one coerce predict.gls to incorporate the fitted correlation structure from the gls object into predictions? In the example below the AR(1) process with phi=0.545 is not used with predict.gls. Is there another function that does this? I'm going to want to fit a few dozen models varying in order from AR(1) to AR(3) and would like to look at the fits with the correlation structure

Hi, I am trying out a generalized least squares method of forecasting that corrects for autocorrelation. I downloaded daily stock data from Yahoo Finance, and am trying to predict Close (n=7903). I have learned to use date functions to extract indicator variables for Monday - Friday (and Friday is missing in the model to prevent it from becoming full rank). When I run the following code...

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

Hi to all, In other statistical software, such as Eviews, it is possible to regress a model with the Least Squares method, assuming that the residuals follow an AR(q) process. For example the resulting regression is something like y = 1.2154 + 0.2215 x + 0.251 AR(1) How is it possible to do the same in R? Thank you very much in advance, Constantine Tsardounis http://www.costis.name

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

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'm trying to perform what should be a reasonably basic analysis of some spatial presence/absence data but am somewhat overwhelmed by the options available and could do with a helpful pointer. My researches so far indicate that if my data were normal, I would simply use gls() (in nlme) and one of the various corSpatial functions (eg. corSpher() to be analagous to similar analysis in SAS)

Dear all, I am a beginner user of R and I tried to fit a gls model with explanatory variables and an AR(1) correlation component using the function "gls" with: correlation = corAR1 (form = ~ 1) It should mean that the residual follows an AR(1) process, isn't it? The problem is that, if I use the funcion "predict" I noticed that the predicted values are the same as if I

Hi, I have fitted a model using a glm() approach and using a gls() approach (but without correcting for spatially autocorrelated errors). I have noticed that although these models are the same (as they should be), the AIC value differs between glm() and gls(). Can anyone tell me why they differ? Thanks, Geertje ~~~~ Geertje van der Heijden PhD student Tropical Ecology School of Geography

Hi there, I am using gls from the nlme library to fit an AR(1) regression model. I am wondering if (and how) I can separate the auto-correlated and random components of the residuals? Id like to be able to plot the fitted values + the autocorrelated error (i.e. phi * resid(t-1)), to compare with the observed values. I am also wondering how I might go about calculating confidence (or

Hi All, I am doing econometric modeling of panel data (fixed effects). We currently use Eviews to do this, but I have discovered a bug in Eviews 7 and am exploring the use of R to build panel data models / longitudinal data models. I looked at the plm package but do not see how I can incorporate AR terms in the model using the plm package. I have an Eviews model with two AR terms, AR(1) and

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

I am using R 2.3.0, Is there a way to get prediction intervals using predict.gls? Any ideas would be appreciated. thanks, Spencer On 11/2/06, Frank McCown <fmccown@cs.odu.edu> wrote: > > I was wondering if anyone knows who should be contacted to add to the R > user-contributed documentation at > > http://www.r-project.org/other-docs.html > > There doesn't

Hi all, In sip.conf: [general] regcontext = autoreg [devabc] regexten = 543 creates "exten=> 543,1,Noop(devabc)" in context autoreg when devabc registers. But I can't use "exten=> _5XX,2,Dial(SIP/${EXTEN})" in the dialplan, because there's no device SIP/543. Now I know I can add a line like "exten=> 543,2,Dial(SIP/devabc)" for each and