[R] Model Based Bootstrap

From: Andreas Klein <klein82517_at_yahoo.de>
Date: Tue, 06 May 2008 01:32:32 -0700 (PDT)

Hello.

Has anyone any idea how a function would look like of a model based bootstrap, when the underlying time series follows an ARIMA(1,1,1)-process? A pure AR-process is no problem, but what is, if the time series need to be differentiated of order one or above and the additional MA-part?

Sample code for a series, which follows a pure AR-process:

#Series y of 192 observations, which follows an AR(1)-process
#Fit of an AR(1)-Model to y

ar.coef <- ar(y)$ar
ar.resid <- ar(y)$resid

#Sampling for mean

y_sample    <- numeric(192)
y_sample[1] <- y[1]
mean_y      <- numeric(10000)

for (i in 1:10000)

{

 for (j in 1:191)

 {

  idx <- sample(2:192,1,replace=TRUE)   y_sample[j+1] <- y_sample[j]*ar.coef+ar.resid[idx]

 }

 mean_y[i] <- mean(y_sample)  

}

What would the function look like if y follows an ARIMA(1,1,1)-process for example or in general if y is a time series, which need to be differentiated and is best modeled with a mixture of AR and MA?

I hope you can help me.

Sincerely
Andreas.

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Tue 06 May 2008 - 08:35:38 GMT