Why not?
I mean, if I understand correctly, the real data is 100 observations every
year, for 15 years. The averages are throwing away information.
Admittedly, one might want to do something to account for the clustering
of cases. Lots of options there--time series models, two-way fixed
effects, and so on, all of which would likely raise the standard errors of
Z1, Z2, and so on to some extent to reflect the loss of precision that
attends the inclusion of more terms in a model. But, none of those
approaches will just throw away the information that is in the 15 year
variation in the data, variation that could be useful (e.g., variation in
gas prices in the United States, 1975-1990)
Am I misunderstanding the original question?
Sam
On Sun, 8 Apr 2007, David Greenberg wrote:
> No, it is not. David Greenberg, Sociology Department, New York University
>
> ----- Original Message -----
> From: "Roy,Suryadipta" <[email protected]>
> Date: Sunday, April 8, 2007 8:54 pm
> Subject: Re: st: An econometric question
> To: [email protected]
>
>
> > Maarten,
> >
> > Thanks so much for your response! Now just to make sure
> > that I am understanding you right, there were initially
> > 1500 cases (no. of year-countries) which was subsequently
> > reduced to 100 cases (no. of countries) by taking the
> > averages of the variables over 15 years. In this
> > situation, will it be alright to report the results I am
> > getting for the regression with 1500 observations (the
> > ones that are significant)?
> >
> > Thanks again!
> > Suryadipta.
> >
> > On Sun, 8 Apr 2007 20:25:18 +0100 (BST)
> > Maarten buis <[email protected]> wrote:
> > > --- "Roy,Suryadipta" <[email protected]>
> > >wrote:
> > >> I have some questions on the interpretation of my
> > >>results.
> > >> I have data on (say) 100 countries over a period of 15
> > >> years, i.e. a total of 1500 observations. Suppose, I am
> > >> running a model of the form:
> > >> y= a0 + a1x1 + a2x2 + a3x3, where y, x1 and x2 are the
> > >> mean values of the original variables (say, z, z1 and
> > >>z2)
> > >> over 15 years for all countries and x3 is a
> > >>time-constant
> > >> variable (say, a region dummy).
> > >>
> > >> Now, when I am running the above regression in the
> > >> original dataset (with 1500 observations), I am getting
> > >> significant results for x1, x2 and x3. Of course, all
> > >>the
> > >> variables remain unchanged for any country over these 15
> > >> years.
> > >>
> > >> On the other hand, if I "collapse" the dataset and run
> > >>the
> > >> regression with 100 observations (for 100 countries),
> > >>none
> > >> of the variables remain significant and the r-square
> > >>goes
> > >> down as well. The mean values for y, x1 and x2 (and x3)
> > >> are however, the same in both the regressions.
> > >>
> > >> I would greatly appreciate some help if understanding as
> > >> to why I am not getting the same results as in the first
> > >> regression.
> > >
> > > If you have more cases, you have more bits of
> > >information going into
> > > your estimate, so you are more confident about your
> > >results, so you are
> > > willing to say that smaller deviations from your null
> > >hypothesis are
> > > non-accidental, i.e. significant. So the question is: If
> > >you want to
> > > regress averages over 15 years of a 100 countries, do
> > >you have a 100
> > > cases (number of countries), or 1500 cases (number of
> > >year-countries)?
> > > The answer (unfortunately) is that you have only a 100
> > >cases. By using
> > > year-countries (1500 cases) you are just duplicating the
> > >same
> > > information, in other words, each year does not add any
> > >new info.
> > >
> > > Hope this helps,
> > > Maarten
> > >
> > >
> > > -----------------------------------------
> > > Maarten L. Buis
> > > Department of Social Research Methodology
> > > Vrije Universiteit Amsterdam
> > > Boelelaan 1081
> > > 1081 HV Amsterdam
> > > The Netherlands
> > >
> > > visiting address:
> > > Buitenveldertselaan 3 (Metropolitan), room Z434
> > >
> > > +31 20 5986715
> > >
> > > http://home.fsw.vu.nl/m.buis/
> > > -----------------------------------------
> > >
> > >
> > >
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