Hey,
Generally speaking -some other people could disagree- I think that including
cluster dummy regressors should be enough to adjust your variance estimates.
It looks weird to put these regressors and a -cluster- option all together
(When I wrote you I did not know you are planning to add cluster dummies as
regressors as well!!).
Besides, I am not sure if your variable group assigns a code for every
cluster firm. Your output says that you have defined 344 clusters of firms
to compute variances, which seems way too much for a total sample size of
1644. That seems to be the reason you don't get a well-computed F test.
I am not aware of what your ultimate goal is but it seems to me that your
estimates look very noisy. You got very wide confidence intervals, which
means very little can be said about the behavior your dependent variable.
Bottom line: if you are confident on your specification, that is, cluster
dummies are included as regressors, don't use the cluster option (which
anyway seems to be wrongly implemented) and only use -robust- alone. If you
have heteroskedastic and/or autocorrelated errors AND your model is
correctly specified, your OLS estimates are still consistent and unbiased.
The robust option -White's correction- would provide you're a consistent
estimator for the variance under heteroskedasticity of unknown form.
Finally, You can also be benefited from the collective wisdom of the Stata
list users.I hope someone else could give you some advice here.
Hope it helps,
Benjamin
-----Mensaje original-----
De: [email protected]
[mailto:[email protected]] En nombre de Dalhia Mani
Enviado el: Tuesday, October 07, 2008 2:53 PM
Para: [email protected]
Asunto: Re: st: RE: SUR correction for autocorrelation
Benjamin,
I ran the regression "y x1 x2, robust cluster(gr)" to control for
clustering among firms in my dataset, and I get the results I was
expecting. However, when I run this regression, the F statistics are
missing, and I am concerned that this means something is wrong with
the regression.
All other aspects of the stata output look fine. However, the F
statistic is blank. See output below.
Any suggestions will be much appreciated.
thanks
dalhia
regress roa_dec2001 firm2 firm3 firm4 firm5 firm6 firm7 cluster1
cluster1_1 cluster2 cluster3 cluster4 cluster5 clus
> ter6 cluster7 cluster8 cluster9 cluster10 overlappingcluster degree
aggregate_constraint prod_count age sum_knowhow ln_
> totassets2001, robust cluster(group)
Linear regression Number of obs =
1644
F( 16, 343) =
.
Prob > F =
.
R-squared =
0.0143
Root MSE =
.73013
(Std. Err. adjusted for 344 clusters in
group)
----------------------------------------------------------------------------
--
| Robust
roa_dec2001 | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------+--------------------------------------------------------------
--
firm2 | .0494928 .0840523 0.59 0.556 -.1158301
.2148157
firm3 | .0348837 .051861 0.67 0.502 -.0671218
.1368893
firm4 | -.0089329 .0124503 -0.72 0.474 -.0334215
.0155557
firm5 | -.0252334 .0236897 -1.07 0.288 -.0718287
.0213619
firm6 | -.0568828 .0481064 -1.18 0.238 -.1515034
.0377378
firm7 | -.0219176 .0636692 -0.34 0.731 -.1471488
.1033137
cluster1 | .0612633 .0790641 0.77 0.439 -.0942482
.2167747
cluster1_1 | -.000552 .0453069 -0.01 0.990 -.0896663
.0885624
cluster2 | -.1239266 .107221 -1.16 0.249 -.33482
.0869668
cluster3 | -.0797388 .0524637 -1.52 0.129 -.1829299
.0234522
cluster4 | -.0382077 .0255735 -1.49 0.136 -.0885083
.012093
cluster5 | -.0474023 .0399954 -1.19 0.237 -.1260693
.0312648
cluster6 | .0263318 .0509641 0.52 0.606 -.0739098
.1265734
cluster7 | -.0835975 .0745768 -1.12 0.263 -.2302829
.0630878
cluster8 | -.0926067 .092746 -1.00 0.319 -.2750292
.0898159
cluster9 | -.0182355 .0544206 -0.34 0.738 -.1252756
.0888045
cluster10 | -.0697966 .0707843 -0.99 0.325 -.2090227
.0694294
overlappin~r | -.0924298 .0661568 -1.40 0.163 -.222554
.0376943
degree | .0022567 .0019908 1.13 0.258 -.0016589
.0061724
aggregate_~t | -.1004189 .1637774 -0.61 0.540 -.4225534
.2217155
prod_count | .0034202 .0022548 1.52 0.130 -.0010148
.0078553
age | -.0008939 .000299 -2.99 0.003 -.001482
-.0003059
sum_knowhow | -.0011635 .0015493 -0.75 0.453 -.0042109
.0018838
ln_tota~2001 | .0470392 .0388523 1.21 0.227 -.0293796
.1234581
_cons | -.1456748 .0676188 -2.15 0.032 -.2786745
-.0126752
----------------------------------------------------------------------------
--
On Sun, Oct 5, 2008 at 12:31 AM, Benjamin Villena Roldan
<[email protected]> wrote:
> 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
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>> *
>> * For searches and help try:
>> * http://www.stata.com/help.cgi?search
>> * http://www.stata.com/support/statalist/faq
>> * http://www.ats.ucla.edu/stat/stata/
>>
>
>
>
> --
> Dalhia Mani
> Department of Sociology
> University of Minnesota
> Office: 1052 Social Sciences
> 267 19th Avenue South, Minneapolis
> MN 55455
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/statalist/faq
> * http://www.ats.ucla.edu/stat/stata/
>
--
Dalhia Mani
Department of Sociology
University of Minnesota
Office: 1052 Social Sciences
267 19th Avenue South, Minneapolis
MN 55455
*
* For searches and help try:
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* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
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* http://www.ats.ucla.edu/stat/stata/
