>>> [email protected] 09/14/05 7:55 PM >>>
it would actually help if you could sent your commands to the list so
that we see what's going on,
best
robert
Of course. Thank you for your time.
My first model is regressing the log of GDP on the year (ie, 1996) and
the square of the year (ie, 3984016). Originally, I ran this model
separately for 30 countries. I then obtained the predicted value of the
log of GDP for each of those countries (by using -predict-) and used it
in a second model.
The suggestion was to interact year and year squared with each of the
countries in the dataset so that I could put them all in one regression,
using the country dummies and the -noconst- option. Seems sensible, but
when I tried it, Stata dropped all the country dummies. Here was my
code (I know there are more parsimonious ways to do this, but...):
*All the countries are given an index from 1 to 30.*
gen mol =1 if index== 1
replace mol =0 if index~= 1
gen arm =1 if index== 2
replace arm =0 if index~= 2
etc. This generated dummies with 1 if the observation belonged to that
country (Moldova is #1, for example).
Then, I interacted the dummies with both year and year squared:
gen yrmol=year*mol
gen yr2mol=yearsq*mol
gen yrarm=year*arm
gen yr2arm=yearsq*arm
etc.
Finally, I ran a regression that looks like:
regress lngdp yrmol yr2mol yrarm yr2arm ... mol arm ... , nocons
where mol = Moldavia, arm = Armenia, and so on.
When I run this, I get the following (truncated):
lnpppc1 | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
mol | (dropped)
arm | (dropped)
yrmol | .2019759 1.003532 0.20 0.841 -1.772746
2.176698
yr2mol | -.0001006 .0005037 -0.20 0.842 -.0010917
.0008904
yrarm | .149713 .4002733 0.37 0.709 -.6379338
.9373598
yr2arm | -.0000744 .0002011 -0.37 0.712 -.0004702
.0003214
However, when I run the following:
regress lngdp year yearsq if index ==1
for example, I do get results. That is what I did in order to get the
predicted variables for the second stage.
Anything jump out at you? Again, thank you for your time and patience.
-Rachel
>>> [email protected] 09/14/05 7:55 PM >>>
it would actually help if you could sent your commands to the list so
that we see what's going on,
best
robert
On 9/14/05, Rachel Bouvier <[email protected]> wrote:
> Hi again. I tried interacting my xs with country specific dummies
and
> running them in a single equation as suggested. Stata is dropping
the
> country dummies, even though I specify the nocons option. (I
remember
> now that this was why I had originally run it in 30 different
equations
> - it works fine that way, but not if I put them all into one
equation.)
> Am I doing something wrong? It could be because xsq is the square
of
> x, but I don't understand why stata would let me do it for an
individual
> country but not together. Sorry for being obtuse. -Rachel
>
> >>> [email protected] 09/13/05 4:50 PM >>>
> a possible solution could be to run in a single model the equation
>
> (1) y = b1 x + b2 xsq
>
> interacting your x's with country specific dummies.
>
> In other words, you could run a fully interactive model which is
> equivalent to running 30 different regressions but in a single
> equation. (make sure you include the country specific dummies too
that
> would account for the constant in your separate regressions and
> specify the nocons option).
>
> hope this helps.
> robert
>
>
> On 9/13/05, Rachel Bouvier <[email protected]> wrote:
> > Dear statalisters *
> >
> > I am confronting a problem much like that described by James
Hardin
> in volume 2, issue 3 of the Stata Journal, "The robust variance
> estimator for two-stage models," where he gave an illustration of
Stata
> code to construct the Murphy-Topel variance estimator.
> >
> > I am using a variable (call it yhat), predicted in a first (series
> of) equations, as a regressor in my second equation.
> >
> > In other words, my first (series of) regressions looked like this:
> > (1) y = b1 x + b2 xsq
> >
> > Then, I predicted yhat from that regression, and used that in a
> second regression:
> > (2) z = b1 yhat + b2 x2 + b2 x3*
> >
> > I say "series of" regressions because I have a panel of 30
countries.
> Rather than run one panel data regression and predict each
country's
> yhat from that, I ran each country as a separate regression, not
wanting
> to assume that they could be pooled. In other words, I ran equation
(1)
> 30 different times, for each country in the dataset. (It seemed to
make
> sense at the time, to both me and my committee!)
> >
> > Therein lies my problem. I would like to adjust the standard
errors
> for the fact that I predicted yhat, but as I ran a different
regression
> for each country, the solution is not as easy as constructing the
> Murphy-Topel estimator. Does anyone have any suggestions? Any help
> would be much appreciated, before I dive into something that is
> undoubtedly over my head. Thanks.
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