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/
