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Re: st: RE: SUR correction for autocorrelation


From   "Dalhia Mani" <dalhia.mani@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: SUR correction for autocorrelation
Date   Tue, 7 Oct 2008 13:52:43 -0500

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
<bvillena@troi.cc.rochester.edu> 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: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Dalhia Mani
> Enviado el: Saturday, October 04, 2008 11:43 PM
> Para: statalist@hsphsun2.harvard.edu
> 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
> <bvillena@troi.cc.rochester.edu> 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: owner-statalist@hsphsun2.harvard.edu
>> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Dalhia Mani
>> Enviado el: Saturday, October 04, 2008 4:48 PM
>> Para: statalist@hsphsun2.harvard.edu
>> 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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>



-- 
Dalhia Mani
Department of Sociology
University of Minnesota
Office: 1052 Social Sciences
267 19th Avenue South, Minneapolis
MN 55455
*
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*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



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