similar to: good procedure to estimate ARMA(p, q)?

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 <---- 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

Hi! Is there any package or function on R to ARMA models (Box & Jenkins, without sazonality and trend) with resources to automatic identification for p and q ? Regards, Raphael Saldanha Brazil [[alternative HTML version deleted]]

Hi, Can anyone explain how the standard error in arima() is calculated? Also, how can I extract it from the Arima object? I don't see it in there. > x <- rnorm(1000) > a <- arima(x, order = c(4, 0, 0)) > a Call: arima(x = x, order = c(4, 0, 0)) Coefficients: ar1 ar2 ar3 ar4 intercept -0.0451 0.0448 0.0139 -0.0688 0.0010 s.e.

Hi all, Suppose I am fitting an arma(p,q) model to a time series y_t. So, my model should contain (q+1) white noise variables. As far as I know, each of them should have the same variance. How do I get the estimate of this variance by running the arma(y) function (or is there any other way)? Appreciate your help. Thanks, Preetam -- Preetam Pal (+91)-9432212774 M-Stat 2nd Year,

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 >

(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

Hi, I'm trying to plot several lag.plots on a page, however the second plot replaces the first one (although it only takes up the upper half as it should): par(mfrow=c(2,1)) a<-sin(1:100) b<-cos(1:100) lag.plot(a) lag.plot(b) What's the trick to this? I'm using R 2.2.1 (2005-12-20 r36812) on Ubuntu Linux. Thanks, Gad -- Gad Abraham Department of Mathematics and

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Hi, I'm using zoo because it can automatically label the months of a time series composed of daily observations. This works well for certain time series lengths, but not for others, e.g.: While: > library(zoo) > plot(zoo(runif(10), as.Date("2005-06-01") + 0:50)) Shows up the months and day of month, > plot(zoo(runif(10), as.Date("2005-06-01") + 0:380))

Hi, I'd like to store large covariance matrices using Matrix classes. dsyMatrix seems like the right one, but I want to specify just the upper/lower triangle and diagonal and not have to instantiate a huge n^2 vector just for the sake of having half of it ignored: Dumb example: M <- new("dsyMatrix", uplo="U", x=rnorm(1e4), Dim=as.integer(c(100, 100))) diag(M) <- 1

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.

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

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

Dear all R user, Please forgive me if my problem is too simple. Actually my problem is basically Statistical rather directly R related. Suppose I have return series ret with mean zero. And I want to fit a Garch(1,1) on this. my is r[t] = h[i]*z[t] h[t] = w + alpha*r[t-1]^2 + beta*h[t-1] I want to estimate the three parameters here; the R syntax is as follows: #

In the summary of the output of arma, there's a number Pr(>|t|), however, I don't know what is its meaning - at least, it doesn't _seem_ to be a Student's t distribution. Reproducible test case: x <- c(0.5, sin(1:9)) reg <- arma(x, c(1,0)) summary(reg) <output> Call: arma(x = x, order = c(1, 0)) Model: ARMA(1,0) Residuals: Min 1Q Median 3Q

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

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]]

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, I have two example packages, test1 and test2, where the only code in them is: setGeneric("predict", function(object, ...) standardGeneric("predict")) (get them from http://www.cs.mu.oz.au/~gabraham/test1.tar and http://www.cs.mu.oz.au/~gabraham/test2.tar) The difference between them is that first does not have a namespace, and loads fine. The second has a namespace

Hi, I'm trying to do PCA on a n by p wide matrix (n < p), and I'd like to get more principal components than there are rows. However, svd() only returns a V matrix of with n columns (instead of p) unless the argument nv=p is set (prcomp calls svd without setting it). Moreover, the eigenvalues returned are always min(n, p) instead of p, even if nv is set: > x <-