similar to: assign() won't work

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))) )

Dear R-helpers, I would appreicate if someone can help me on the transfer parameter in ARIMAX and also see what I am doing is correct. I am using ARIMAX with 2 Exogeneous Variables and 10 years data are as follows: DepVar Period, depVar, IndepVar1 Period, indepVar1, IndepVar2 Period, indepVar2 Jan 1998,708,Jan 1998,495,Jan 1998,245.490 Feb 1998,670,Feb 1998,421.25,Feb 1998,288.170 Mar

Another pathological test. arima does not crash for that series that crashes arma: arima(c(2.01, 2.22, 2.09, 2.17, 2.42), order=c(1,0,0)) However, arima crashes for this: arima(c(1.71, 1.78, 1.95, 1.59, 2.13), order=c(1,0,0)) arima seems pretty consistent in its crashing behaviour, since crashing for one series means crashing for all affine series: lets.crash.arima <- c(71, 78, 95, 59,

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 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, I have been looking at arima.sim to simulate the output from an ARMA model fed with a normal and uncorrelated input series but I cannot find a way to pass an intercept / constant into the model. In other words, the model input in the function allows only for the AR and MA components but I need to pass a constant. Can anyone help? Thanks Paolo [[alternative HTML version deleted]]

I'd like to fit an ARMA(1,1) model to some data (Federal Reserve Bank interest rates) that looks like: ... 30JUN2006, 5.05 03JUL2006, 5.25 04JUL2006, N &lt;---- here! 05JUL2006, 5.25 ... One problem is that holidays have that "N" for their data. As a test, I tried fitting ARMA(1,1) with and without the holidays deleted. In other words, I fit the above data

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.

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

Hello - Here's what I'm trying to do. I want to fit a time series y with ARMA(1,1) + GARCH(1,1), there are also an exogeneous variable x which I wish to include, so the whole equation looks like: y_t - \phi y_{t-1} = \sigma_t \epsilon_t + \theta \sigma_{t-1} \epsilon_{t-1} + c x_t where \epsilon_t are i.i.d. random variables \sigma_t^2 = omega + \alpha \sigma_{t-1}^2 + \beta

Dear R users, I need to fit an ARMA model. As far as I've seen, EACF (extended ACF) is not available in R. 1. Let's say I fit a series of ARMA models in a loop. Given the code/output included below, how do I pull 'Model' and 'Fit' (AIC) from each summary() so that I can combine them into an array/data frame to be sorted by AIC? 2. Apart from EACF, are you aware perhaps

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

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

Hello, I try to simulate an ARMA-model using R, but I didn't find any function to generate such timeseries. In Splus there is the function arima.sim which generates AR-, MA- and ARIMA-series. Is there any similar in R? Best regards, Frank Beimfohr -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

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,

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

Hi, I want to assign the ar1 , ma 1 and the intercept estimated by the following<http://r.789695.n4.nabble.com/assign-estimated-values-td4658138.html#>code to three variables a, b and c respectively. Can anyone help me with this please? code: a0 = 0.05; a1 = 0.1; b1 = 0.85 nu = rnorm(2500) epsi = rep(0, 2500) h = rep(0, 2500) for (i in 2: 2500) { h[i] = a0 + a1 * epsi[i-1]^2 + b1 * h[i-1]

> 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,Could somebody tell me what is the difference between  theese 3 calls of functionsarma(x,order=c(1,0)), arima(x,order=c(1,0,0)) ar(x,order=1)?I expected same residuals of theese three models,but unexpectably for the first two R requiredinitial value of something (what?)...Thanks in advance!  [[alternative HTML version deleted]]