similar to: STARMA model (Space-Time ARMA)

Je suis étudiant en DEA et j'élabore un mémoir dans lequel j'applique le modèle STARMA , Pouvez vous ,s'il vous plait, m'envoyer l'algorithme de calcul ces STACF et STPACF de ce model afin de débuter mon estimation de ce modéle sur R Merci d'avance. --------------------------------- [[alternative HTML version deleted]]

Hi all, I am fitting an ARMA(1,(1,4)) model. y(t) = a*y(t-1) + e(t) + b1*e(t-1) + b4*e(t-4) > arma1.14 <- arma(series, lag=list(ar=1, ma=c(1,4)), + include.intercept = F, qr.tol = 1e-07) works fine: Coefficient(s): ar1 ma1 ma4 0.872 -0.445 0.331 I want to forecast 50 periods. I could not find a 'predict' function for ARMA models. I

Hello, I am running an ARMA model to run forecast for changes in S&P 500 prices. My ARMA calculations look as follows armacal <- arma( spdata, order = c(0,4), lag = list(ma = c(1,2,4)) ) Output: Call: arma(x = spdata, order = c(0, 4), lag = list(ma = c(1, 2, 4)) ) Coefficient(s): ma1 ma2 ma4 intercept -0.073868 0.058020 -0.081292 0.007082 All's

Hello. I've got a problem with arma/xreg. I would like to get a better model-fit by implenting some external explanatory variable, so I thought I can implement it by expand the arima-function with an xreg-argument: I have two stationary data vectors y and x of length 201: y <-

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 dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

On Tue, Oct 13, 2009 at 5:06 PM, Rolf Turner <r.turner@auckland.ac.nz>wrote: > > Not clear to me what the OP really wants. Perhaps the seasonal > model is what's required; perhaps an arima(1,0,4) model with > theta_2 and theta_3 constrained to be 0. The latter can be > achieved with > > arima(x,order=c(1,0,4),fixed=c(NA,NA,0,0,NA,NA)) > > Or perhaps

Hi, I'm trying to model an ARMA(1,[1,4]), i.e. I want only lags 1 and 4 of the Moving Average part. It's the '[1,4]' part that is giving me a problem. I've tried different arma's and arima's in different packages, namely: packages tseries, fArma, FinTS, timeSeries, TSA, Zelig, ds1, forecast For example, with package FinTS: > ( ARIMA(y, order=c(1,0,c(1,4))) )

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

Hi, i am learning R recently and find it very helpful in time series model. In ARMA model, given (p,q) it can get the estimation of a[i] and b[j] easily with arima() function. X[t] = a[1]X[t-1] + ... + a[p]X[t-p] + e[t] + b[1]e[t-1] + ... + b[q]e[t-q] but in my recent data model, i met a problem. In the ARMA model, p and q are fixed, but there are some constraints in the parameters

> It might clarify your thinking to note that a seasonal ARIMA model > is just an ``ordinary'' ARIMA model with some coefficients > constrained to be 0 in an efficient way. E.g. a seasonal AR(1) s = > 4 model is the same as an ordinary (nonseasonal) AR(4) model with > coefficients theta_1, theta_2, and theta_3 constrained to be 0. You > can get the same answer as from

Hello, I would like to compute ARMA and AR using arima-function in R. My question is: If I have Null=zero values in my data, what should I do? Remove ? or doesn't matter for ARIMA-models and I can estimate my coefficients including zero values in data in arima-function in R ? What is the better way? How to manage the data for ARIMA estimation? Thank you. [[alternative HTML version

Hi given an ARMA process and the AR and MA coefficients I need the residuals. arima() calculates the residuals together with the best AR and MA coefficients, but I need the coefficients to take known values. In S-PLUS there is a function arima.filt(). Is there something similar in R? Thanks for any help, Matthias Budinger

I have the following time series model for prediction purposes *Loss_t = b1* Loss_(t-1) + b2*GDP_t + b3*W_(t-1)* where W_t is the usual white noise variable. So this is similar to ARMA(1,1) except that it also contains an extra predictor, GDP at time t. I have only 20 observations on each variable except GDP for which I know till 100 values. And most importantly,I have also calculated

Hello I have a XP on my Pc ,and I would like to install the splancs package on my machine what is the step for implementing them on my library? --------------------------------- [[alternative HTML version deleted]]

Hi R-Community, so far I dealt with univariate processes and used the function "arima" to estimate an ARMA(1,1)-model. For multivariate processes there are the functions "estVARXar" and "estVARXls" from package "DSE". But how can I estimate an VARMA(1,1)-model, or even better determine the orders and estimate the parameters? Much thanks in advance, Hagen

Hello, BODY { font-family:Arial, Helvetica, sans-serif;font-size:12px; } Does any one know if there are any functions/packages available in R for robust fitting of ARMA time series models (e.g., similar to the function arima.rob() in S-PLUS)? Many thanks and kind regards, Isabella Isabella R. Ghement, Ph.D. Ghement Statistical Consulting Company 301-7031 Blundell Road,

Why the standadard errors of the coefficientes of the arma models fited by using the arima procedure in the stats package doesnt coincide with that of S+, minitab or SAS? -- Este mensaje ha sido analizado por MailScanner en busca de virus y otros contenidos peligrosos, y se considera que est? limpio.

When I do ARMA(2,2) using one lag of LCPIH data This is eview result > > *Dependent Variable: DLCPIH > **Method: Least Squares > **Date: 08/12/11 Time: 12:44 > **Sample (adjusted): 1970Q2 2010Q2 > **Included observations: 161 after adjustments > **Convergence achieved after 14 iterations > **MA Backcast: 1969Q4 1970Q1 > ** > **Variable Coefficient Std.

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods