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RE: st: RE: Replacing missing values


From   "joe J." <otharain@hotmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   RE: st: RE: Replacing missing values
Date   Thu, 22 Apr 2004 11:35:11 +0000

Thank you very much Nick.


From: "Nick Cox" <n.j.cox@durham.ac.uk>
Reply-To: statalist@hsphsun2.harvard.edu
To: <statalist@hsphsun2.harvard.edu>
Subject: RE: st: RE: Replacing missing values
Date: Thu, 22 Apr 2004 12:26:58 +0100

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
n.j.cox@durham.ac.uk

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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