Now, using the model that we built,

we can make predictions going out into the future.

So, we use two years for calibration, can we go beyond our calibration period?

Absolutely.

We can forecast out as far as we want.

And that's what you would keep on doing.

Testing the model and seeing when is it time to estimate it.

So we had worked with three years of data initially.

We said we're going to use the first two years for

calibration tested on one year of data.

Once we found the specification that was appropriate, let's estimate our

log linear regression model on all of the initial data that we have, and

now let's try to forecast out a couple of months using that model.

And you see from the dotted line here that we're doing pretty well.

Not perfect, we still have this slight under-prediction in those summer months.

But we've built a pretty good model here in terms of being able to forecast

the future, and all we've included is a trend and month specific dummy variables.

So there's not necessarily a need to put in so many predictors into your model.

Rather, you want to give some thought to what's the most parsimonious model that I

can construct.

And one that's going to have some staying power that I'm not going to have to

re-estimate, necessarily, in the short term.