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
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