[R] forecasting linear regression from lagged variable

[Gabor Grothendieck]

On Wed, Nov 30, 2011 at 4:40 PM, AaronB <aaron at communityattributes.com> wrote:
> I'm currently working with some time series data with the xts package, and
> would like to generate a forecast 12 periods into the future. There are
> limited observations, so I am unable to use an ARIMA model for the forecast.
> Here's the regression setup, after converting everything from zoo objects to
> vectors.
>
> hire.total.lag1 <- lag(hire.total, lag=-1, na.pad=TRUE)
> lm.model <- lm(hire.total ~ case.total + hire.total.lag1)
>
> hire.total is a monthly historical time series from Jan 2010 to Oct 2011.
> hire.total.lag1 is the same time series, lagged 1 period backwards.
> case.total is a monthly historical time series from Jan 2010 to Oct 2011,
> plus forecasts forward another 12 periods.
>
> I'd like to use this model to forecast hire.total for the next period, and
> use each successive prediction of hire.total as the lag1 "observation" for
> the next prediction. I have enough "observed" values for case.total to
> forecast out as far as I need. I might be able to construct this using a
> loop, but I have a feeling it will be messy and slow. Any suggestions?
>

Its not clear that you can't use an arima model.  You would use the
n.ahead= argument of predict.Arima.

The dyn and dynlm packages handle lagged lm's of zoo objects but you
will have to do a predict and then append the prediction to the data
and repeat in a loop as you describe.  If you want to be careful about
error terms then its more complex.

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