Hi Dalhia,
I reread my answers. I'm sorry I wasn't that clear. You could implement
robust cluster variance estimators in simple regressions
-regress y x1 x2, robust cluster(gr)-
The option -cluster- is available in most estimations commands in Stata. The
cluster variable -gr- defines groups of firms of a similar characteristic.
The errors are correlated among the cluster, but they are independent across
clusters. See Wooldridge "Econometric Analysis of Cross-Sectional and Panel
Data" page 134 for further details.
Prais-Weinstein is not a good idea because you have to define that some
firms are "closer"to other in some sense. The correlation among errors
decays in the "distance" among firms. Unless you have a good reason your
observations need to be ordered in a very specific way, this procedure
doesn't make sense. In time series for instance, the time order among
observations is obvious, so in that case it will work.
Regarding to the second point, your system is clearly a simultaneous
equation model, since you have endogenous variables on the right-hand side
of equations 2 and 3. You need to check your equations are identified before
running any procedure. This is very important. Any introductory textbook in
econometrics such as Gujarati or Maddala, could help you to address this
question.
After you have done this, you'll need instrumental variables to estimate the
structural form. Then you have several estimators you could choose from
two-stage least square (2SLS), three-stage least square (3SLS), and even the
Limited-information-Max-Likelihood (LIML) which is preferable when you have
"weak instruments". You could implement these estimators using the Stata
commands -ivreg- or -ivreg2-.
I hope I was clearer than I was before.
Best,
Benjamin
-----Mensaje original-----
De: [email protected]
[mailto:[email protected]] En nombre de Dalhia Mani
Enviado el: Saturday, October 04, 2008 11:43 PM
Para: [email protected]
Asunto: Re: st: RE: SUR correction for autocorrelation
Benjamin,
Thanks. This is useful but I'd like to clarify and make sure I
understand your comments. I apologize if these are really elementary
questions. I'm still trying to figure this stuff out.
1) The data is not time series. I have data about firms for a single
time period, and I also have data indicating which firms belong to
which cluster of firms. From what I understand, you are suggesting
that I should use the Prais-Winston command in stata, with a "cluster"
option?? Did I understand you correctly?
2) I am a bit confused about whether I should be using SUR or
simultaneous equations.
My three equations look something like this:
y1=f(X+Z)+e_1
y2=g(X+Z)+y1+e_2
y3=g(X+Z)+y1+y2+e_3
This set of equations looks like simultaneous equations since
independent variables in one equation become dependent variables in
another. However, I also seem to remember that in cases where all
equations use the same exogenous variables (X and Z), I should be
using SUR.
Thanks for your suggestions and help. I appreciate it.
dalhia
On Sat, Oct 4, 2008 at 4:41 PM, Benjamin Villena Roldan
<[email protected]> wrote:
> Hi
> You don't mention whether your data is a cross-section or a panel. That's
> quite important.
> Regarding (1) you have clusters of firms, so you can estimate your
variance
> matrix using the option cluster. Cochrane-Orcutt works for time
> autocorrelation, so you need a measure of "proximity"among the firms
within
> a cluster. I think you don't have that. In time-series, that measure is
> given by the time dimension.
> Regarding (2), I think you need to think carefully about the relationship
> among your equations. Are you estimating structural or reduced forms
> equations? For instance, is accounting performance included as a regressor
> in your stock-market valuation?. If it is you have a simultaneous equation
> model. If it's not, you're estimating a reduced form, but you have to be
> very careful about the interpretation of your marginal effects.
>
> I hope it helps
>
> Benjamin
>
> -----Mensaje original-----
> De: [email protected]
> [mailto:[email protected]] En nombre de Dalhia Mani
> Enviado el: Saturday, October 04, 2008 4:48 PM
> Para: [email protected]
> Asunto: st: SUR correction for autocorrelation
>
> hi,
>
> I have a set of equations that specify the relationship between a set
> of independent variables and outcome variables - survival, stockmarket
> and accounting performance. I have two questions that I would
> appreciate your help with.
>
> 1) The data is at the firm level. Some of the firms belong to
> clusters of firms, and hence I expect autocorrelation in the residuals
> when I run each equation separately. Therefore, I plan to use the the
> Prais-Winston command, specifying the Cochran-Orcutt option in stata
> to correct for autocorrelation when running each equation separately.
> I think this approach is correct, however I am not a 100% sure, and
> will appreciate it if you think otherwise and can correct me.
>
> 2) I also need to use a simultaneous unrelated regression (SUR) model
> since it is possible that the set of equations are related (e.g.
> survival might be related to performance). How do I correct for
> autocorrelation for the SUR model in stata?
>
> Any suggestions and advice will be much appreciated.
>
> thanks
> dalhia
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--
Dalhia Mani
Department of Sociology
University of Minnesota
Office: 1052 Social Sciences
267 19th Avenue South, Minneapolis
MN 55455
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