On Sun, 19 Dec 2010 01:34:25 +0100, <

[hidden email]> wrote:

> Dear researchers

>

> I would like to ask, if there is a way how to make in R

> (fGarch,tseries,..) EXPOST prediction(quasi future). Correct me, if I am

> wrong, but the command predict(x,n.ahead) makes EXANTE predictions(true

> future prediction) only. In contrast with Eviews, where forecast

function

> produces expost results. Another idea, is to divide the testing set into

> training and validation sets, estimate for example ARMA coefficients for

> training set and use them with validations set to produce expost

> predictions. In pseudocode it looks like this:

>

> testing_set (1:100)

> training_set(1:90)

> validation_set(91:100)

>

> arma_coef<-fit(training_set,~arma(1,1))

>

> expost_prediction<- arma_coef * validation_set

>

> Is this a way how to produce EXPOST predictions, or my approach is

> completely wrong? I beleive, there is much easier way how to do it in R,

> but I just cant find the satysfying solution by myself. Thank you any

help.

I believe what you are asking is:

"How do I use a pre-set (trained) set of ARMA or GARCH coefficients on a

new data set without changing the model?"

This is not ex post 'prediction'. The model fitting is ex post. predict

is *always* using the current model to predict future movements (ex ante)

on observations in the time series that come in after the model is fit.

In fGarch, at least, there is no way to do this. See a list post from me

approximately one+ year ago asking about predict and pre-fitted values for

fGarch, and Yohan's helpful reply on the modifications that would be

necessary to fGarch.

In any GARCH model, the conditional volatility may be (and usually is)

displayed on the prior data.

for arma(), I don't know the answer, but I suspect it will involve some

coding on your part.

Regards,

- Brian

--

Brian G. Peterson

http://braverock.com/brian/Ph: 773-459-4973

IM: bgpbraverock

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