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Re: st: RE: SUR correction for autocorrelation


From   "Dalhia Mani" <dalhia.mani@gmail.com>
To   "Rodrigo Alfaro A." <ralfaro@bcentral.cl>
Subject   Re: st: RE: SUR correction for autocorrelation
Date   Tue, 7 Oct 2008 21:27:54 -0500

Rodrigo, Thanks I just saw your response.

This is helpful. I will try this.

dalhia

On Tue, Oct 7, 2008 at 4:07 PM, Rodrigo Alfaro A. <ralfaro@bcentral.cl> wrote:
>
> ///
>
> Dalhia,
>
> Your regression with clusters seems similat to FE+regression. I found
> that F is missing under your setting, but you could deal with that using
> -xtreg, fe cluster()- instead of -reg, cluster()-. The following example
> shows you my point using the dataset available in Stata. Regression with
> -reg- has the same coefficients than -xtreg-, but the latter computes
> the standard errors under the assumption of FE at the cluster level.
> Note that standard errors are different because degree of freedom are
> computed dropping the id's dummies.
>
> I hope this helps you,
> Rodrigo.
>
> **** Begin example
> sysuse auto, clear
> reg price mpg turn foreign, cluster(foreign)
> xtreg price mpg turn, fe cluster(foreign) i(foreign)
> **** End example
>
>
> -----Mensaje original-----
> De: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Benjamin
> Villena Roldan
> Enviado el: Martes, 07 de Octubre de 2008 03:41 p.m.
> Para: statalist@hsphsun2.harvard.edu
> Asunto: RE: st: RE: SUR correction for autocorrelation
>
> 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: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] En nombre de Dalhia Mani
> Enviado el: Tuesday, October 07, 2008 2:53 PM
> Para: statalist@hsphsun2.harvard.edu
> 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
> <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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-- 
Dalhia Mani
Department of Sociology
University of Minnesota
Office: 1052 Social Sciences
267 19th Avenue South, Minneapolis
MN 55455

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