similar to: How to convert ARMA process to infinite AR?

Hi I am trying to fit a time series data.It gives a AR(2) model using the ar function and ARMA(1,1) model using autoarmafit function in timsac package.How do I know which is the correct underlying model? pls help -- View this message in context: http://r.789695.n4.nabble.com/AR-vs-ARMA-model-tp4639015.html Sent from the R help mailing list archive at Nabble.com.

Hi, Let theta be the coefficient in AR(1) model, the components of the correlation structure is equal to 1 for times s = t, and theta^|t-s| for t > s. Could someone do me a favour to give some ideas on the correlation structure in AR(2) model and correlation structure of ARMA(p,q), as well? Thank you Fir [[alternative HTML version deleted]]

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 am having problems with the package dse. I just installed R 2.2.1 and reinstalled all packages. I am running Windows XP Pro with all updates. Below there are two examples of error messages generated when trying to execute some simple programs. The code was taken directly from the package documentation. Any help on this will be greatly appreciated. Merry Christmas Fernando

Dear Sir, I think I found a bug in the function arma.sim, which is defined in the help page of the function filter: arma.sim <- function(n, ar = NULL, ma = NULL, sigma = 1.0) { x <- ts(rnorm(n+100, 0, sigma^2), start = -99) if(length(ma)) x <- filter(x, ma, sides=1) if(length(ar)) x <- filter(x, ar, method="recursive") as.ts(x[-(1:100)]) } I am using R

Hello all, I would like how to modelized a time serie with AR-ARCH process. It can be used arma and garch functions in tseries package for build ar process or a garch process, but how can it be modelized a ar-garch model ? Thanks [[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

Hi, I am new to using R for Time series analysis. I was wondering if there are any functions that can convert ARMA or ARIMA time series into their corresponding AR or MA time series representations (by calculating the corresponding AR or MA coefficients). Thanks a lot Kris.

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

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

R-guRus , ARMA function in tseries, seems to be calculating the AR coeff 's as coef <- lm(xx[,1]~xx[,lag$ar+1])$coef [*snipped* from around line 77,] I'd like to modify this model with another term somewhat in these lines lm(xx[,1] ~xx[,lag$ar+1]+mvgsignal)$coef where mvgsignal is a moving average signal based on some indicators, the question is could i simply hack into

Thank you so much all for your invaluable inputs. On Sat, Jun 4, 2011 at 3:36 AM, Patrick Burns <patrick at burns-stat.com> wrote: > A common thing to do is the Ljung-Box > test on the residuals. ?For garch it > would be the residuals squared. > > Actually for garch it should be the > rank of the squared residuals -- see >

> 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

(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

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

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

Hello Following some standard textbooks on ARMA(1,1)-GARCH(1,1) (e.g. Ruey Tsay's Analysis of Financial Time Series), I try to write an R program to estimate the key parameters of an ARMA(1,1)-GARCH(1,1) model for Intel's stock returns. For some random reason, I cannot decipher what is wrong with my R program. The R package fGarch already gives me the answer, but my customized function

Dear R experts, I am trying to estimate an ARMA 2,2 model with garch errors. I used the following code on R 2.9. #library library(fGarch) #data data1<-ts(read.table("C:/Users/falcon/Desktop/Time Series/exports/goods1.csv"), start=c(1992,1), frequency=12) head(data1) #garch garchFit(formula.mean= ~arma(2,2),formula.var=~garch(1,1), data=data1) but get this error: >

Hi, is there a way in the arma function to exclude coefficients for the estimation. For example I estimated an ARMA(2,2) model to data where only the coefficients of the second order have been significant. Therefore I would like to estimate the ARMA(2,2) model without the 1,1-coefficients. Thanks a lot! Regards, Ralf -- View this message in context: