# RE: st: RE: Replacing missing values

 From "joe J." To statalist@hsphsun2.harvard.edu Subject RE: st: RE: Replacing missing values Date Thu, 22 Apr 2004 11:12:47 +0000

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?

Thanks
Joe

```From: "Nick Cox" <n.j.cox@durham.ac.uk>
To: <statalist@hsphsun2.harvard.edu>
Subject: st: RE: Replacing missing values
Date: Thu, 22 Apr 2004 11:22:02 +0100

You have first to consider the possibility
that your data do _not_ lend themselves
to linear interpolation. If, for example,
investment varies irregularly, as seems
plausible given that is a response to both
macroeconomic and microeconomic factors, then
gaps in a very peaky time series will be difficult to
interpolate accurately.

That said, so long as all your data are
positive, interpolating on the logarithms and
exponentiating the result is a sure
mathematical way of avoiding negative predictions.

Real economists will be able to add more. My
economics education stopped with A-level (British
high school leaving qualification) Economics
in 1969.

Nick
n.j.cox@durham.ac.uk

joe J.

> I have a panel (unbalanced) dataset of manufacturing plants
> for several
> years. Investment series is missing for a lot of plants. I tried the
> statacode -ipolate-, which uses linear interpolation and
> extrapolation
> procedures. But these have resulted in many negative values.
> Is there any
> way one could restrict -ipolate- from extrapolating negative
> values or are
> there superior methods to replace missing values?

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