Thank you all for your responses. I am doing a within-sample forecast.
I am working through your helpful responses.
Thanks again!
On 8/31/07, Robert A Yaffee <[email protected]> wrote:
> Erika,
> How you proceed depends on whether you are performing an ex ante or an out-of-sample
> forecast. Then you have to decide whether you want your forecast to be based on an
> iterative projection or a structural forecast.
> You have to examine the driving variable to see whether there is inherant autocorrelation
> in it that needs prewhitening or another form of neutralization (using the Pankratz linear
> transfer functon approach).
> If you are using ex ante forecasting, you must control for that first and predict the x over
> the forecast horizon. Then you can insert that data and proceed to predict the y. Then you
> need to predict the residuals to analyze them to see whether they have remaining arma errors
> that need to be modeled.
> If you are using an out-of-sample forecast, Clive's approach is fine assuming that all of the
> aforementioned assumptions are fulfilled.
> You also have the option of resorting to the prais, newey, or the
> var y , exog(x) command
> Regards,
> Bob Yaffee
>
>
>
>
> Robert A. Yaffee, Ph.D.
> Research Professor
> Shirley M. Ehrenkranz
> School of Social Work
> New York University
>
> home address:
> Apt 19-W
> 2100 Linwood Ave.
> Fort Lee, NJ
> 07024-3171
> Phone: 201-242-3824
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> homepage: http://homepages.nyu.edu/~ray1/
>
> ----- Original Message -----
> From: Clive Nicholas <[email protected]>
> Date: Friday, August 31, 2007 5:08 am
> Subject: Re: st: Time Series/ arima postestimation- How to forecast more than one-step-ahead?
> To: [email protected]
>
>
> > Erika Morris wrote:
> >
> > > I have time series data and would like to use levels of one variable
> > > (X) to forecast changes in another variable (Y) over multiple periods.
> > > In other words, I want to estimate something like the following
> > > equation:
> > >
> > > Yt+k - Yt = b*Xt + error,
> > >
> > > where k>1.
> > >
> > > It looks like the "arima" command and "predict" postestimation do
> > > something similar, but based on my reading of the Time Series manual
> > > they only calculate one-step-ahead forecasts. I would like to use the
> > > actual value of Xt to forecast changes in Y over longer periods (k).
> >
> > Actually, you should be able to do this for future 'out-of-sample'
> > periods using -arima-, so long as you have information on the
> > regressors. This example from Kit Baum's 2004 survey lecture to the UK
> > Stata Users Group should help you out.
> >
> > webuse friedman2, clear
> > label var pc92 "Real Personal Consumption"
> > arima pc92 L.pc92 L(0/1).m2 if tin(,1981q4)
> > * static (one-step-ahead) 20-quarter forecast
> > predict consump_st if tin(1982q1,1986q4)
> > * dynamic (recursive) 20-quarter forecast
> > predict consump_dyn if tin(1982q1,1986q4), dynamic(q(1982q1))
> > tsline pc92 consump_st consump_dyn if tin(1982q1,1986q4), scheme(economist)
> > legend(cols(1) stack)
> >
> > The last bit is added on. The statistics in the Y variable are not
> > first differences, but imagine that they are! I've used this to fit
> > ARIMA models of my own, and it works very well.
> >
> > --
> > Clive Nicholas
> >
> > [Please DO NOT mail me personally here, but at
> > <[email protected]>. Thanks!]
> >
> > Baum K (2004) "SUGUK 2004 Invited Lecture: Topics In Time Series
> > Modelling With Stata", available at
> > http://ideas.repec.org/p/boc/usug04/7.html
> > *
> > * For searches and help try:
> > * http://www.stata.com/support/faqs/res/findit.html
> > * http://www.stata.com/support/statalist/faq
> > * http://www.ats.ucla.edu/stat/stata/
> *
> * For searches and help try:
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>
*
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