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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 >> * >> * 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/

**Follow-Ups**:**RE: st: RE: SUR correction for autocorrelation***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

**References**:**st: SUR correction for autocorrelation***From:*"Dalhia Mani" <dalhia.mani@gmail.com>

**st: RE: SUR correction for autocorrelation***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

**Re: st: RE: SUR correction for autocorrelation***From:*"Dalhia Mani" <dalhia.mani@gmail.com>

**RE: st: RE: SUR correction for autocorrelation***From:*"Benjamin Villena Roldan" <bvillena@troi.cc.rochester.edu>

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