00,lambdas1[1],lambdas1[2],lambdas1[3],lambdas1[4])
>
> #Fit an AR(1)
> gld_simulated <- arima(x_sim,order=c(1,0,0))
> gld_simulated
>
> #Code ends
>
>
> -Original Message-
> From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
> Sent: Wed
t;
> #Fit an AR(1)
> gld_simulated <- arima(x_sim,order=c(1,0,0))
> gld_simulated
>
> #Code ends
>
>
> -Original Message-
> From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
> Sent: Wednesday, November 28, 2007 11:37 AM
> To: Rodriguez, Pedro
&
,0,0))
gld_simulated
#Code ends
-Original Message-
From: Prof Brian Ripley [mailto:[EMAIL PROTECTED]
Sent: Wednesday, November 28, 2007 11:37 AM
To: Rodriguez, Pedro
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Simulate an AR(1) process via distributions? (without
specifying a model specification
On Wed, 28 Nov 2007, [EMAIL PROTECTED] wrote:
> Is it possible to simulate an AR(1) process via a distribution?
Any distribution *of errors*, yes. Of the process values, not in general.
> I have simulated an AR(1) process the usual way (that is, using a model
> specification and using the rando
Dear All,
Is it possible to simulate an AR(1) process via a distribution?
I have simulated an AR(1) process the usual way (that is, using a model
specification and using the random deviates in the error), and used the
generated time series to estimate 3- and 4-parameter distributions (for
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