This can happen when you use a linear model for something that is nonlinear
(in this case, that has inequality constraints). The best known example, in
my field at least, is the linear probability model. Have a look at any
econometrics textbook that discusses the LPM, and you'll see that the
drawback of predicted probabilities that are <0 or >1 is mentioned. This is
one reason why logits and probits are usually preferred to the LPM.
Hope this helps.
--Mark
Quoting yi jiang <[email protected]>:
> thank you Nick. even though i plot the data and the fitted data,
> the
> problem is still there. let me give some examples:
> 1. X1>0, however, the fitted value of X1 from first stage has
> negative
> observation.
> 2. 0<X1<1, however, the fitted value of X1 from first stage has
> observation bigger than 1.
> you name it.
> I guess it doesn't matter, but it does look questionable in terms
> of
> how to explaine the meaning of X1.
>
> On Mon, 21 Feb 2005 22:47:39 -0000, Nick Cox <[email protected]>
> wrote:
> > Why not plot the data and your fit,
> > if that makes sense?
> >
> > Nick
> > [email protected]
> >
> > yi jiang
> >
> > > I'm doing a two stage regression. I'm predicting X1 (X1 are
> all
> > > positive) at the first stage and then use the predicted value of
> X1 at
> > > the second stage.
> > > However, the predicted value of X1 shows some negative value,
> which
> > > makes no sense in terms of data--X1 cannot be negative. any
> thoughts?
> >
> > *
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> >
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>
Prof. Mark Schaffer
Director, CERT
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS
tel +44-131-451-3494 / fax +44-131-451-3294
email: [email protected]
web: http://www.sml.hw.ac.uk/ecomes
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