similar to: ARIMA

I'd like to play with ARIMA models of stock prices, but I am a complete novice. Could some kind soul explain the relationship among packages "ts", "tseries", "dse", "dse2", and "fracdiff"? Are they 'competing' products or does one depend on another? Where would be the best place for a novice to begin? Thanks for any advice. PS. I

Hello, I would like to study time series with ARIMA and GARCH models. I installed R-Plus and its libraries but when I try to execute the function arima0, It answers that the function does not exist. Could you help me or give me references of papers dealing with arima and garch in R-Plus? Thanks Beno?t, ___________________________________ Mr. Beno?t LACHERON Rue de l'industrie, 44, 1040

I did a regression with ARMA errors using arima0 with ari<-arima0(y,order=c(2,0,2),xreg=reg1,delta=-1) or ari<-arima0(y,order=c(2,0,2),xreg=reg1) where reg1 is the matrix of the regressors and when I see diag(ari$var.coef) I get negative terms. Do you know what this mean ? I try to change transform.pars to 0 or 1 but this crash R on Windows. Is it possible to test the significativity

What is the difference between arima and arima0?

Hi there, Does anyone know how to predict ARMA? It doesn?t have either predict or forecast methods. I found couple of packages called fbasic and fseries at http://www.itp.phys.ethz.ch/econophysics/R/, which has ?arma.predict? in it, but it doesn?t seem to be working. Any help in this regard would be appreciated. Thanks in advance. Regards Skanda Kallur "Prediction is very difficult,

Hi all, Can I run a cointegrating regression, for example delta Xt=a1(Yt-1-cXt-1)+E1t and delta Yt=-b1(Yt-1-cXt-1)+E2t with R were Xt and Yt are non stationary time series at t a,b,c are parameters and E1t and E2t are error terms at t. Yt-Xt is stationary Any suggestions are welcome. Best regards, /fb -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

HI, there, When i use R, i tried to use function arima(), it complains: Error: couldn't find function "arima" But when I type "help.search("arima") ", I got arima() poped up.. arima(ts) ARIMA Modelling of Time Series arima.sim(ts) Simulate from an ARIMA Model arima0(ts) ARIMA Modelling of Time Series -- Preliminary

I'm using the fixed argument in arima. Shouldn't ar4, ar5, and ar6 display as zero in the output? Call: arima(x = window(log(hhprice), start = c(1990, 1), end = c(2003, 3)), order = c(7, 1, 0), xreg = window(ts.union(exa1 = lag(exa, -1), exa12 = lag(exa, -12), exb1 = lag(exb, -1), exc1 = lag(exc, -1), exc12 = lag(exc, -12)), start = c(1990, 1), end = c(2003, 3)),

Dear All, R 1.9.1, Windows When copying and pasting a few lines from the 'predict.Arima' help, I get an error message: > data(lh) > predict(arima(lh, order = c(3,0,0)), n.ahead = 12) Error in eval(expr, envir, enclos) : Object "xreg" not found On the other hand, the following is OK: > data(lh) > predict(arima0(lh, order = c(3,0,0)), n.ahead = 12) $pred Time

Hi, I have a suggestion for the fonction arima and arima0. I think you should not call the constant an intercept because it creates confusion. It is not really an intercept but a mean. For an AR(1) the intercept mu should be defined as: X(t)=mu + phi X(t-1) + e(t) What you call intercept mu is rather defined as (X(t)-mu) = phi (X(t-1)-mu)) + e(t) which is not a common way to define an

Hi, I am trying to estimate an ARIMA model in the case where I have some specific knowledge about the coefficients that should be included in the model. Take a classical ARIMA (or even ARMA) model: P(B) X(t) = Q(B) epsilon(t), where X(t) is the data, epsilon is a white noise, B is the backward operator and P and Q are some polynoms. Additionally, assume that you know in advance how P and Q

Could someone please give a brief explanation, or pointer to an explanation, of the following error: > arima(ts.growth, order = c(1,0,0),include.mean=T) Error in arima(ts.growth, order = c(1, 0, 0), include.mean = T) : non-stationary AR part from CSS and why it does not arise with > arima0(ts.growth, order = c(1,0,0)) Many thanks ____________________________ Dr. Daniel P. Bebber

Hi, I am trying to forecast a model using predict.Arima I found arima model for a data set: x={x1,x2,x3,...,x(t)} arima_model = arima(x,order=c(1,0,1)) I am forecasting the next N lags using predict: arima_pred = predict(arima_model,n.ahead = N, se.fit=T) If I have one more point in my series, let's say x(t+1). I do not want to recalibrate themodel, I just want to forecast the next N-1

Howdy What I 'd like to analyze with a large data on building permits is to find time series effect of urban policy on buildings as well as cross-sectional effects in any. In 1990 the specialZone urban policy was introduced. I guess that the effects of this specialZone policy would be different from countys. There are counties that do not welcome this specialZone forced to design it. One of

Hi, Before I re-invent the wheel, is there a function in R similar to S+'s arima.sim, i.e., a function that simulates arima processes. ts and tseries packages don't seem to have such function, but I may have overlooked it. Thank you for your time, Alvaro Novo R Version 1.1.1 SuSE 6.4 Linux KDE 2.0 Beta 5 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hello, The "arma" function in the "tseries" package allows estimation of models with specific "ar" and "ma" lags with its "lag" argument. For example: y[t] = a[0] + a[1]y[t-3] +b[1]e[t-2] + e[t] can be estimated with the following specification : arma(y, lag=list(ar=3,ma=2)). Is this possible with the "arima" function in the

I am trying to understand how to fit an ARMAX model with the arima function from the stats package. I tried the simple data below, where the time series (vector x) is generated by filtering a step function (vector u, the exogenous signal) through a lowpass filter with AR coefficient equal to 0.8. The input gain is 0.3 and there is a 0.01 normal white noise added to the output: x <- u

hallo to all I've to calculate an arima model and I need only the first and 365 th parameter and also the sar1 and the intercept, so I'm traing with: arima(X,order=c(365,0,0),seasonal=list(order=c(1,0,0),..),fixed=c(NA,rep(0,363),NA,NA,NA),transform.pars=F) but the error answer is: Error in polyroot(z) : polynomial degree too high (49 max) also there are problems in allocating memory

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)

Dear R People: Is there a package for arima modeling of multiple time series, please? I think that Dr. Paul Gilbert may have one, but I'm drawing a blank on the package name. Thanks in advance, Sincerely, Erin -- Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston - Downtown mailto: erinm.hodgess at gmail.com