similar to: simulation of autoregressive process

Dear Rolf, thank you for reply, I am interested with non random numbers. To be more precise, I would like to obtain two vectors a and b with a=(1,1,1,......,1,1) and b=(0,0,........,0,0). The length of a and b is 10000. Thank you ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts

Dear R people, what functions generate respectively vectors with each element is respectively zero and one. sorry for my credulous questions and many thanks in advance. ____________________________________________________ Écoutez gratuitement le nouveau single de Noir Désir et découvrez d'autres titres en affinité avec vos goûts musicaux

Hi everybody, I'am very interested with the basic structural model of time series. So I used the command "StructTS" but I failed to obtain a desirable output, in fact when I write in R Console the following lines: > x=(1,2,3,4,5,2,25,14,12,13,11,6,9,24,12,13,14,12,12,14,11,12,14,15,20,21,22,23,21,25,28) >(fit <- StructTS(x,type = "BSM")) I obtained the following

Dear R users, I have the joined txt file in the following direct directory C:// I have written the following lines: library(urca); PIBTUN<-read.table("C:/AF.txt", header=F); ur.df(PIBTUN,type='none',lags=1) but I have obtained the following message: Error in embed(z, lags) : 'x' is not a vector or matrix I don't What's the problem, can you please help me Thank

Dear R users, I would like to construct a matrix of seasonal dummy variables, such matrix can be written as follows(i.e format(T,4)) 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 . . . . . . . . etc I have written the following small program:

Hi friends, I would like to construct a matrix of seasonal dummies with number of rows (observations)=100. such matrix is written as follows:[1 0 0 0;0 1 0 0;0 0 1 0;0 0 0 1;1 0 0 0;0 1 0 0;0 0 1 0;0 0 0 1;etc...] . I wrote the following program: T=100 br=matrix(0,T,4) { for (i in 1:T) for (j in 1:4) if i==j br[i,j]=1 if else (abs(i-j)%%4==0 br[i,j]=1 else br[i,j]=0 } z<-br z but

Hi, I'm a phd student of economics in Tunisia who is intersted now with the seasonal unit roots test of Canova and Hansen. So I've installed the package "uroot" in RGUI. I use R 2.4.0. The problem when I apply the function to my data, I've always a message from RConsole that it is impossible to find the function CH.test. Are there problems as for the use of this package?

Dear friends, I installed the package “uroot” then I wrote library (uroot) and I entered a series entitled extp, in spite of that the problem persite. In short,here are what I wrote and the message that I obtained: > library(uroot) > extp=c(1,3,10,14,12,5,8,12,13,15,9,8,7,10,9,7,3,10,3,11,12,7,4,9,8,10,20,17) > CH.test (wts=extp, frec=c(1,1), f0=1, DetTr=FALSE, ltrunc=NULL) Erreur dans

Hi friends, I have a file.xls entitled "Dali" which is composed of two columns: the first is entitled "imp" and the second is entitled:"exp". I putted The file "Dali" in the following way:C:/programfiles/R 2.4.0. I have used the following command:>Dali<-read.table("Dali.xls", header=T) but I can't read this file from R console. What can

Dear All, I have a short question. Is there any readily available function that could generate either an ARMAX model or, more generally, an AutoRegressive Distributed Lag model? I am looking for a function that is similar to armaSim() function in fArma package. Thank you. MP

Hello, in my doctoral thesis i try to model time series crash count data with an inar(1)-process, but i have a few problems in writing the r-code. is there someone, who works with inar-processes. i would be very grateful, if someone gives me some ideas in writing the code. nazli

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:

folks, does anyone know if the SEMIFAR model has been implemented in R? i see that there's a S-FinMetrics function SEMIFAR() that does the job, but I have no access to that software. essentially, this semiparametric fractional autoregressive model introduces a deterministic trend to the FARIMA(p,d,0) model (which, as i understand it, takes care of the random trend and short and long memory).

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

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 wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

I am having difficulty fitting a mgcv::gamm model that includes both a random smooth term (i.e. 'fs' smooth) and autoregressive errors. Standard smooth terms with a factor interaction using the 'by=' option work fine. Both on my actual data and a toy example (below) I am getting the same error so am inclined to wonder if this is either a bug or a model that gamm is simply unable

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, I am using the mAr package to calibrate an Multivariate model (size 3, order 12). I am trying to do the two following things: 1. I would like to calibrate the model using not a single time series, but several of them: each time series should be seen as one "independent" realisation of the mAr process; for instance this happens when you have a time series with lacking data

Hi,I'm just wondering if there is any package for testing cointegration with ADL model. I saw a bunch of packages and list of email thread. There seemed to be no such a specific method. I am following this paper on how to test using ADL but I don't have a tool. http://www.wiwi.uni-frankfurt.de/~hassler/ha-wo.pdfAny help would be really appreciated. Thank you.Taco [[alternative HTML