similar to: regressor & autoregressive error?

Hello, Does anybody know if the function arima with an external regressor (xreg) applies the auto correlation on the dependant variable or on the residuals. In comparison with SAS (proc autoreg), it seems that the auto correlation applies on the residuals but i'd like to have the confirmation. I want to estimate: Y[t] = a[1]*X[t] + a[2] + E[t] with E[t]=b[1]*E[t-1] Should I use : arima(Y,

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hi, I want to predict using airma, but I want to predict using t-2 or t-3, instead of t-1 right now the arima() function doesn't allow me to do that, it will alwasy return with variable t-1 , what is the way to skip that variable? thanks &regards -- View this message in context: http://r.789695.n4.nabble.com/arima-forecast-without-the-first-order-of-auto-regressor-tp4643412.html Sent

Hi, I would like to make a replication of 10 of a linear, first order Autoregressive function, with respect to the replication of its innovation, e. for example: #where e is a random variables of innovation (from GEV distribution-that explains the rgev) #by using the arima.sim model from TSA package, I try to produce Y replicates, with respect to every replicates of e, #means for e[,1], I want

Dear R users, I am trying to find a package that can estimate an autoregressive model for discrete data. I am imagining a Poisson or Gamma process in which the mean (say mu) follows a process such as mu_t = a + b*x + c*mu_{t-1} Suppose I have data on the time-series Poisson outcomes and x and would like to obtain ML estimates for b and c. Does anyone know of a package that can do this

Hi all there I am enjoying R since 2 weeks and I come to my first deadlock, il am trying to use predict.Arima in the ts package. I get a "Error in cbind(...) : cannot create a matrix from these types" -- Start R session ----------------------------------------------------- > fitdiv <- arima(data, c(2, 0, 3), xreg = y ) ; print(fitdiv) Call: arima(x = data, order = c(2, 0, 3),

Hello everybody, I've a question about "autoregressive Regressionmodels". Let Y[1],.....,Y[n], be a time series. Given the model: Y[t] = phi[1]*Y[t-1] + phi[2]*Y[t-1] + ... + phi[p]*Y[t-p] + x_t^T*beta + u_t, where x_t=(x[1t],x[2t],....x[mt]) and beta=(beta[1],...,beta[m]) and u_t~(0,1) I want to estimate the coefficients phi and beta. Are in R any functions or packages for

Dear R users, I would like to simulate, for 20000 replications, an autoregressive process: y(t)=0.8*y(t-1)+e(t) where e(t) is i.i.d.(0,sigma*sigma), Thank you in advance ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

Here is my problem: Autoregressive models are very interesting in forecasting consumptions (eg water, gas etc). Generally time series of this type have a long history with relatively simple patterns and can be useful to add external regressors for calendar events (holydays, vacations etc). arima() is a very powerful function but kalman filter is very slow (and I foun difficulties of estimation)

Hi everyone I've got a problem regarding the arima() and the ar() function for autoregressive series. I would simply like to combine them. To better understand my question, I first show you how I'm using these two functions individually (see file in the attachement). 1) apply(TSX,2, function(x) ar(na.omit(x),method="mle")$order # this function finds the optimal

Hi, Using arima on data that are in a data frame, especially when adding xreg, would be much easier if the arima function contained 1) a "data=" argument 2) the possibility to include the covariate(s) in a formula style. Ideally the call could be something like > arima(symptome, order=c(1,0,0), xreg=~trait01*mesure0, data=anxiete) ( or arima(symptome~trait01*mesure0,

Hello everyone, Hope you all are doing great! I have been fitting arima models and performing forecasts pretty straightforwardly in R. However, I wanted to add a couple of regressors to the arima model to see if it could improve the accuracy of the forecasts but have had a hard time trying to do so. I used the following R function: arima(x, order = c(0, 0, 0), seasonal = list(order = c(0, 0,

Hello - I need to do simple linear autoregressive model with R software for my thesis. I looked into all your documentation and I am not able to find anything too helpful. Can someone help me with the codes? Thanks Emil [[alternative HTML version deleted]]

I'm trying to do the following out of sample regression with autoregressive terms and additional x variables: y(t+1)=const+B(L)*y(t)+C(1)*x_1(t)...+C(K)*x_K(t) where: B(L) = lag polynom. for AR terms C(1..K) = are the coeffs. on K exogenous variables that have only 1 lag Question 1: ----------- Suppose I use arima to fit the model:

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,

Thomas, may I also suggest, from the Documentation>Contributed section of CRAN, "Econometrics in R" by Grant Farnsworth http://cran.at.r-project.org/doc/contrib/Farnsworth-EconometricsInR.pdf (see the chapter on Time series) and, in case you can read Italian, "Analisi delle serie storiche con R" by Vito Ricci http://cran.at.r-project.org/doc/contrib/Ricci-ts-italian.pdf

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates &lt;- as.Date(data.df[,1]) selection&lt;-which(dates&gt;=as.Date("1986-1-1") &amp; dates&lt;=as.Date("2007-12-31")) dep &lt;- ts(data.df[selection,c("dep")]) indep.ret1

Hi, I am trying to replicate Engle and Manganelli's paper Conditional Autoregressive Value at Risk (CAViaR) by Regression Quantiles. I have the Matlab code which I cannot get to work as I have never used Matlab before, does anyone know if there is the same code available to estimate the CAViaR models in R? Thanks, Shane -- View this message in context:

Hi everyone, I'm trying to include an external regressor in my ARIMA model but am having some problems with the data format in R. I've named my dependent variable of interest "count" and the external regressor "abc". The external regressor is a binary variable. Here are the contents of "abc" and the model I've attemped (along with its error message):

Hey, all, I may just be missing something, but I'm trying to construct a temporal autoregression with an independant variable other than just what is happened at a previous point in time. So, the model structure would be something like y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a*x(t) I'm even considering a model of y(t)=b0+b1*y(t-1)+b2*y(t-2)...+a1*x(t)+a2*x(t-1)... So, my data looks like