R help - Nov 2009 - AR Simulation with non-normal innovations - Correct

Dear Users,

I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the
innovations are supposed to follow a t-GARCH(1,1) proccess.

By t-GARCH I want to mean that:

e_t=n_t*sqrt(h_t) and
h_t=ct2+a*(e_t)^2+b*h_t-1.

where n_t is a random variable with t-Student distribution.

If someone could give some guidelines, I can going developing the model.
I did it in matlab, but the loops are very slowly, so I would like to try R.

Thanks in advance,

Rick
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Thanks Andreas.
This is just the start point I was needing.

Best,

Rick


From: Andreas Hary 
Sent: Tuesday, November 03, 2009 7:19 AM
To: Ricardo Gonçalves Silva 
Subject: Re: [R] AR Simulation with non-normal innovations - Correct


Have a look at function arima.sim. It allows you to specify a random number
generator, so you could try something like the following:

arModel <- list(ar = 0, ma = 0, order = c(0, 1, 0))
tGarchGen <- function(a, b, c) {
  # your stuff here, must return a vector of random deviates
}
arima.sim(arModel, n = 100, rand.gen = tGarchGen)

If you would like to generate a bunch of series, say 200, all at once try

mySeries <- replicate(200, arima.sim(arModel, n = 100, rand.gen = tGarchGen))


HTH,

Andreas






2009/11/2 Ricardo Gonçalves Silva <ricardogs@terra.com.br>

  Dear Users,

  I would like to simulate an AR(1) (y_t=ct1+y_t-1+e_t) model in R where the
innovations are supposed to follow a t-GARCH(1,1) proccess.

  By t-GARCH I want to mean that:

  e_t=n_t*sqrt(h_t) and
  h_t=ct2+a*(e_t)^2+b*h_t-1.

  where n_t is a random variable with t-Student distribution.

  If someone could give some guidelines, I can going developing the model.
  I did it in matlab, but the loops are very slowly, so I would like to try R.

  Thanks in advance,

  Rick
         [[alternative HTML version deleted]]

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