I can't say what would be better. Tritely,
interpolation works well when you have a few
gaps in very smooth series, and no crude
interpolation method works well otherwise.
I wrote -cipolate- on SSC partly for
a climatological problem (gaps in
temperature time series) for which -ipolate-
seemed too crude, but in practice my tests
based on omitting real data randomly and
seeing which gave the better reconstruction
did not in fact indicate much better performance
by -cipolate-. That is not very surprising
mathematically for small gaps.
I'll stress (again) for anyone listening
that -cipolate- is not a spline method.
That is, cubic interpolation is not
cubic spline interpolation.
You have to try it and see. No remote
counselling can say what is better for
your data.
Nick
[email protected]
joe J.
> Thanks Nick. True, linear interpolation is not a great idea.
> Another option
> is to regress investment series on related variables (eg.
> fuel) and time
> trend and use the fitted values to replace missing values.
> But I think this
> would be okay if I am dealing with capital stock rather than
> investment.
>
> In fact I toyed with the idea of cubic interpolation, and
> wonder if the
> fluctuating character of investment decisions is a
> justification for using
> it?
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