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. <[email protected]> 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: [email protected]
> [mailto:[email protected]] En nombre de Benjamin
> Villena Roldan
> Enviado el: Martes, 07 de Octubre de 2008 03:41 p.m.
> Para: [email protected]
> 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: [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:
> * 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/
>
> ********************************************************************************
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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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