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From |
Mario Jose <mariojose276@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: comparing equality of coefficients from two subsamples |

Date |
Fri, 22 Feb 2013 18:20:16 +0000 |

Dear Rebecca, Many thanks for your helpful comments. I definitely catch the difference between both approaches. As I do not believe that the full model is different for both subsamples, I decided to adopt the approach you firstly referred, which assumes same disturbance variance. I really appreciated your help. Thank you. Best, MJ 2013/2/21 Rebecca Pope <rebecca.a.pope@gmail.com>: > The FAQ link was intended to be helpful in a "first-prinicples" sense. > I sent it because you seemed to not understand what Jay was saying > about constraining variances & it provided a simple introduction. You > won't be able to use those exact steps with your problem however, not > least because -aweight-s aren't allowed with -xtreg-. > > Now, let's try to clarify what you are wanting before proceeding any > further because I want to make sure that we're clear on the use of > "interaction". > > Say, for example that you are interested in the model > log(wage) = intercept + tenure + tenure^2 + not_smsa + wks_ue > where not_smsa indicates that the respondent doesn't live in a > metropolitan area and wks_ue is the number of weeks she was unemployed > in the previous year. This data is from -webuse nlswork-, the example > given with -xtreg-. > > Now, suppose that you think that the effect of wks_ue differs by > whether or not the respondent lives in an urban area. For this, you > have a simple interaction term. (You can think of this like your > policy indicator). > The Stata syntax for this is: > xtreg ln_w tenure c.tenure#c.tenure i.not_smsa##c.wks_ue, fe > > Now, suppose you further hypothesize that the model above does not > apply equally to southern areas. The model could differ in multiple > ways, but the two that are of interest > concern the south somehow moderating the effect of unemployment and > rural residence. You can approach this in one of two ways. > > The first is to simply model the difference with respect to not_smsa > and wks_ue; all other effects are the same. The second does not > constrain any of the coefficients to be equal across groups, here > south/not south. > The first syntax is: xtreg ln_w tenure c.tenure#c.tenure > i.south##i.not_smsa##c.wks_ue, fe > This is what you say you want in your most recent post. > > The second approach though is what you have written: > xtreg ln_w tenure c.tenure#c.tenure i.not_smsa##c.wks_ue if south==0, fe > xtreg ln_w tenure c.tenure#c.tenure i.not_smsa##c.wks_ue if south==1, fe > > If you estimate these equations, you get different parameter estimates > for _all_ terms by "south". This is why I said that you were working > with a fully-interacted model. To understand this, note that you must > estimate the two equations above _as one_ in order to test whether > rural unemployment differs in the south (or your government policy > differs by firm type). > The correct Stata syntax is: > xtreg ln_w i.south#c.tenure i.south#c.tenure#c.tenure > i.south##i.not_smsa##c.wks_ue, fe > > Do not take "simply" above to mean that it is somehow inferior. I just > mean that the model has fewer parameters to estimate. Your choice of > specification must be theory-driven. If you think that approach 2 is > incorrect, then nothing stops you using approach 1. However, that > isn't what you indicated you were estimating when you wrote 2 separate > equations. > > With all of these approaches, you get 1 error term for both groups. Is > this a problem? It depends on your groups. You have to look at your > data and decide. If you decide you shouldn't constrain the variance, > you'll need to choose an appropriate approach at that point. > > Now, what do you observe with respect to the coefficients? Probably > that the "pooled" regression does not exactly reproduce the > coefficients of the separate regressions with -xtreg, fe-. This > shouldn't surprise you. -xtreg, fe- is estimating a model on the > "demeaned" data, the so-called "within" estimator. When you pool the > observations, you alter the calculation of the mean within the j-th > unit. This occurs because there are some respondents, in this example, > who have lived in and out of the south. If that weren't the case, > "1.south" would be dropped from the FE part of our model when we > pooled results and we would be left with a single overall intercept. > > Quite apart from that, if you submitted > xtreg ln_w tenure c.tenure#c.tenure i.south##i.not_smsa##c.wks_ue, fe > > thinking you were going to get the same results as: > xtreg ln_w tenure c.tenure#c.tenure i.not_smsa##c.wks_ue if south==0, fe > xtreg ln_w tenure c.tenure#c.tenure i.not_smsa##c.wks_ue if south==1, fe > > you would be wrong, even if you were using simple linear regression > because you are working with fundamentally different views of how your > grouping variable relates to the other covariates. > > I hope this helps, > Rebecca > > > > On Wed, Feb 20, 2013 at 4:10 PM, Mario Jose <mariojose276@gmail.com> wrote: >> Thank you Rebecca for the links, they were very useful to understand >> the previous Jay's comment. >> I have implemented the strategy of Bill Gould (allowing for different >> variances), but it appeared the message of error "weight must be >> constant within id"... Anyway I do not want to introduce interactions >> with all independent variables but to only one. >> >> Below I expose what the specific problem I have. >> >> I have a panel sample of firms, and in the middle of the period >> (2004) it was implemented by the government a specific fiscal >> measure. I want to test whether this measure had impacts on the >> profits reported by firms. As I think that the measure had impacts in >> a specific subsample of firms, I divided the sample in two subsamples >> - group1 group2 (splitted according the debt/assets ratio of firms). >> >> I run the model for the two groups separately: >> xtreg, Y x1 control1 control2 ... i.pos i.pos#c.x1 if group==1, fe >> xtreg, Y x1 control1 control2 ... i.pos i.pos#c.x1 if group==2, fe. >> >> (pos is binary taking value 1 for years after the implementation of the policy) >> >> and I obtain the following estimates for group 1 and 2, respectively: >> >> *******output excerpt************ >> >> ----------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t P>|t| [95% >> Conf. Interval] >> ------------------+---------------------------------------------------------------- >> x1 | -2.053274 .5641935 -3.64 0.000 -3.159248 >> -.9473006 >> control1 | .5904103 .0267907 22.04 0.000 .5378933 .6429273 >> control2 | .0947558 .0233539 4.06 0.000 .0489758 .1405358 >> ... | -.0234459 .2617354 -0.09 0.929 -.5365189 >> .4896271 >> year dum.. | >> 1.pos | -.5814072 .1512517 -3.84 0.000 -.877902 -.2849124 >> 1.pos#c.x1 | 1.256448 .4183398 3.00 0.003 .4363875 2.076508 >> _cons | -6.099231 1.766059 -3.45 0.001 -9.561191 -2.637272 >> ------------------+---------------------------------------------------------------- >> sigma_u | 2.1744991 >> sigma_e | .77651905 >> rho | .88690051 (fraction of variance due to u_i) >> ----------------------------------------------------------------------------------- >> >> >> ----------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t P>|t| [95% >> Conf. Interval] >> ------------------+---------------------------------------------------------------- >> x1 | -2.047585 .6997248 -2.93 0.003 -3.41921 >> -.6759593 >> control1 | .4552402 .0232387 19.59 0.000 .4096868 .5007936 >> control2 | .028412 .0110095 2.58 0.010 .0068306 .0499933 >> ... >> year dum .. | >> 1.pos | -.4291118 .1817098 -2.36 0.018 -.7853059 -.072917 >> 1.pos#c.x1 |.6220617 .5078439 1.22 0.221 -.3734318 1.617555 >> cons | -7.341474 1.606579 -4.57 0.000 -10.49075 -4.192201 >> ------------------+---------------------------------------------------------------- >> sigma_u | 2.4369753 >> sigma_e | .70849863 >> rho | .92206421 (fraction of variance due to u_i) >> ----------------------------------------------------------------------------------- >> >> **********end of excerpt************* >> >> These results are in the direction of the predicted, but when I pooled >> the sample for me to compare the coefs, the estimates appear to be >> significantly different. They are as follows: >> >> *******output excerpt************ >> -------------------------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t >> P>|t| [95% Conf. Interval] >> ---------------------------------+---------------------------------------------------------------- >> x1 | -1.601963 .5324727 -3.01 >> 0.003 -2.645681 -.5582453 >> control1 | .5435240 .0232387 19.59 0.000 >> .4096868 .5007936 >> control2 | .03976 .0110095 2.58 0.010 >> .0068306 .0499933 >> ... | >> year dum .. | >> 1.pos | -.382873 .1487651 -2.57 0.010 >> -.6744726 -.0912734 >> pos#c.x1 | .5273469 .4331443 1.22 0.223 >> -.3216739 1.376368 >> 1.group | .2575 .175552 1.47 0.142 >> -.0866054 .60 >> 1.group#c.x1 | -.8550352 .5470408 -1.56 0.118 >> -1.927308 .217238 >> 1.group#pos | -.2539677 .1681945 -1.51 0.131 >> -.5836514 .075716 >> 1.goup#pos#c.x1 | .8948809 .528096 1.69 0.090 >> -.140258 1.93002 >> _cons | -6.485282 1.161574 -5.58 0.000 >> -8.762123 -4.208441 >> ---------------------------------+---------------------------------------------------------------- >> sigma_u | 2.2954577 >> sigma_e | .76123454 >> rho | .90092029 (fraction of variance due to u_i) >> >> **********end of excerpt************* >> >> Do you find something wrong with the last equation? >> >> I would appreciate any help. >> Best >> MJ >> > <snip> > > On Wed, Feb 20, 2013 at 4:10 PM, Mario Jose <mariojose276@gmail.com> wrote: >> Thank you Rebecca for the links, they were very useful to understand >> the previous Jay's comment. >> I have implemented the strategy of Bill Gould (allowing for different >> variances), but it appeared the message of error "weight must be >> constant within id"... Anyway I do not want to introduce interactions >> with all independent variables but to only one. >> >> Below I expose what the specific problem I have. >> >> I have a panel sample of firms, and in the middle of the period >> (2004) it was implemented by the government a specific fiscal >> measure. I want to test whether this measure had impacts on the >> profits reported by firms. As I think that the measure had impacts in >> a specific subsample of firms, I divided the sample in two subsamples >> - group1 group2 (splitted according the debt/assets ratio of firms). >> >> I run the model for the two groups separately: >> xtreg, Y x1 control1 control2 ... i.pos i.pos#c.x1 if group==1, fe >> xtreg, Y x1 control1 control2 ... i.pos i.pos#c.x1 if group==2, fe. >> >> (pos is binary taking value 1 for years after the implementation of the policy) >> >> and I obtain the following estimates for group 1 and 2, respectively: >> >> *******output excerpt************ >> >> ----------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t P>|t| [95% >> Conf. Interval] >> ------------------+---------------------------------------------------------------- >> x1 | -2.053274 .5641935 -3.64 0.000 -3.159248 >> -.9473006 >> control1 | .5904103 .0267907 22.04 0.000 .5378933 .6429273 >> control2 | .0947558 .0233539 4.06 0.000 .0489758 .1405358 >> ... | -.0234459 .2617354 -0.09 0.929 -.5365189 >> .4896271 >> year dum.. | >> 1.pos | -.5814072 .1512517 -3.84 0.000 -.877902 -.2849124 >> 1.pos#c.x1 | 1.256448 .4183398 3.00 0.003 .4363875 2.076508 >> _cons | -6.099231 1.766059 -3.45 0.001 -9.561191 -2.637272 >> ------------------+---------------------------------------------------------------- >> sigma_u | 2.1744991 >> sigma_e | .77651905 >> rho | .88690051 (fraction of variance due to u_i) >> ----------------------------------------------------------------------------------- >> >> >> ----------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t P>|t| [95% >> Conf. Interval] >> ------------------+---------------------------------------------------------------- >> x1 | -2.047585 .6997248 -2.93 0.003 -3.41921 >> -.6759593 >> control1 | .4552402 .0232387 19.59 0.000 .4096868 .5007936 >> control2 | .028412 .0110095 2.58 0.010 .0068306 .0499933 >> ... >> year dum .. | >> 1.pos | -.4291118 .1817098 -2.36 0.018 -.7853059 -.072917 >> 1.pos#c.x1 |.6220617 .5078439 1.22 0.221 -.3734318 1.617555 >> cons | -7.341474 1.606579 -4.57 0.000 -10.49075 -4.192201 >> ------------------+---------------------------------------------------------------- >> sigma_u | 2.4369753 >> sigma_e | .70849863 >> rho | .92206421 (fraction of variance due to u_i) >> ----------------------------------------------------------------------------------- >> >> **********end of excerpt************* >> >> These results are in the direction of the predicted, but when I pooled >> the sample for me to compare the coefs, the estimates appear to be >> significantly different. They are as follows: >> >> *******output excerpt************ >> -------------------------------------------------------------------------------------------------- >> | Robust >> Y | Coef. Std. Err. t >> P>|t| [95% Conf. Interval] >> ---------------------------------+---------------------------------------------------------------- >> x1 | -1.601963 .5324727 -3.01 >> 0.003 -2.645681 -.5582453 >> control1 | .5435240 .0232387 19.59 0.000 >> .4096868 .5007936 >> control2 | .03976 .0110095 2.58 0.010 >> .0068306 .0499933 >> ... | >> year dum .. | >> 1.pos | -.382873 .1487651 -2.57 0.010 >> -.6744726 -.0912734 >> pos#c.x1 | .5273469 .4331443 1.22 0.223 >> -.3216739 1.376368 >> 1.group | .2575 .175552 1.47 0.142 >> -.0866054 .60 >> 1.group#c.x1 | -.8550352 .5470408 -1.56 0.118 >> -1.927308 .217238 >> 1.group#pos | -.2539677 .1681945 -1.51 0.131 >> -.5836514 .075716 >> 1.goup#pos#c.x1 | .8948809 .528096 1.69 0.090 >> -.140258 1.93002 >> _cons | -6.485282 1.161574 -5.58 0.000 >> -8.762123 -4.208441 >> ---------------------------------+---------------------------------------------------------------- >> sigma_u | 2.2954577 >> sigma_e | .76123454 >> rho | .90092029 (fraction of variance due to u_i) >> >> **********end of excerpt************* >> >> Do you find something wrong with the last equation? >> >> I would appreciate any help. >> Best >> MJ >> >> 2013/2/20 Rebecca Pope <rebecca.a.pope@gmail.com>: >>> Jay has given you important advice as it pertains to the group >>> residual variances. >> >>> You are correct that Wooldridge gives an explanation of interaction >>> terms. He also notes that a fully interacted model (as I assume you >>> will be estimating since your initial post seemed to suggest that you >>> expect different coefficients for all covariates for males and >>> females) assumes group error homogeneity (pg 245 of the 4th ed). >>> Unfortunately, there doesn't appear to be any discussion, at least in >>> that section, of how to address heteroskedasticity between the groups. >>> I didn't read through the rest of the book >> >>> You might want to take a look at this FAQ by Bill Gould: >>> http://www.stata.com/support/faqs/statistics/pooling-data-and-chow-tests/ >>> >>> And these slides from a talk by Bobby Gutierrez: >>> http://www.stata.com/meeting/fnasug08/gutierrez.pdf >>> >>> Only you can see your data and judge whether the constrained variance >>> model is appropriate or not. I wouldn't just dismiss the issue out of >>> hand though. >>> >>> Rebecca >>> >>> On Wed, Feb 20, 2013 at 5:47 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>> Thanks you for comments. Testing for equality of coefficients from >>>> different subsamples, as suggested by Marteen, can be solved by >>>> interactions. >>>> There is an excellent explanation of the procedure in Wooldridge: >>>> Introd.Econometrics ModernApproach; pp. 243-246 and pp. 449-450 and in >>>> the following link: >>>> http://www.stata.com/support/faqs/statistics/chow-tests/ >>>> >>>> Best, >>>> MJ >>>> >>>> 2013/2/18 JVerkuilen (Gmail) <jvverkuilen@gmail.com>: >>>>> As someone else indicated, your syntax is odd. >>>>> >>>>> The main question I have is whether you want to allow for different >>>>> group residual variances. If not, interaction. If so, then I guess the >>>>> easiest approach would be -suest-. >>>>> >>>>> On Mon, Feb 18, 2013 at 11:15 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>>>> Dear Statalisters, >>>>>> >>>>>> I have tryed to solve the question below, searching for help in the >>>>>> Stata Archiv without too much success... >>>>>> >>>>>> I have estimated a fixed effects linear regression for two different >>>>>> groups on my sample (say, sex male/female), using this strategy: >>>>>> xtreg dv iv, if sex==male >>>>>> xtreg dv iv, if sex==female >>>>>> >>>>>> I am interested in testing whether or not the coefficient b1 is >>>>>> identical to each other in the two subsamples. >>>>>> >>>>>> I would really appreciate any help. >>>>>> Regards >>>>>> MJ >>>>>> * >>>>>> * For searches and help try: >>>>>> * http://www.stata.com/help.cgi?search >>>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> >>>>> >>>>> >>>>> -- >>>>> JVVerkuilen, PhD >>>>> jvverkuilen@gmail.com >>>>> >>>>> http://lesswrong.com/ >>>>> >>>>> "Everybody loves progress but nobody likes change." ---Fortune cookie, 1/13/13. >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>> * http://www.ats.ucla.edu/stat/stata/ >>>> >>>> 2013/2/18 JVerkuilen (Gmail) <jvverkuilen@gmail.com>: >>>>> As someone else indicated, your syntax is odd. >>>>> >>>>> The main question I have is whether you want to allow for different >>>>> group residual variances. If not, interaction. If so, then I guess the >>>>> easiest approach would be -suest-. >>>>> >>>>> On Mon, Feb 18, 2013 at 11:15 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>>>> Dear Statalisters, >>>>>> >>>>>> I have tryed to solve the question below, searching for help in the >>>>>> Stata Archiv without too much success... >>>>>> >>>>>> I have estimated a fixed effects linear regression for two different >>>>>> groups on my sample (say, sex male/female), using this strategy: >>>>>> xtreg dv iv, if sex==male >>>>>> xtreg dv iv, if sex==female >>>>>> >>>>>> I am interested in testing whether or not the coefficient b1 is >>>>>> identical to each other in the two subsamples. >>>>>> >>>>>> I would really appreciate any help. >>>>>> Regards >>>>>> MJ >>>>>> * >>>>>> * For searches and help try: >>>>>> * http://www.stata.com/help.cgi?search >>>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> >>>>> >>>>> >>>>> -- >>>>> JVVerkuilen, PhD >>>>> jvverkuilen@gmail.com >>>>> >>>>> http://lesswrong.com/ >>>>> >>>>> "Everybody loves progress but nobody likes change." ---Fortune cookie, 1/13/13. >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/statalist-faq/ >>>> * http://www.ats.ucla.edu/stat/stata/ >>> >>> >>> >>> On Wed, Feb 20, 2013 at 5:47 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>> Thanks you for comments. Testing for equality of coefficients from >>>> different subsamples, as suggested by Marteen, can be solved by >>>> interactions. >>>> There is an excellent explanation of the procedure in Wooldridge: >>>> Introd.Econometrics ModernApproach; pp. 243-246 and pp. 449-450 and in >>>> the following link: >>>> http://www.stata.com/support/faqs/statistics/chow-tests/ >>>> >>>> Best, >>>> MJ >>>> >>>> 2013/2/18 JVerkuilen (Gmail) <jvverkuilen@gmail.com>: >>>>> As someone else indicated, your syntax is odd. >>>>> >>>>> The main question I have is whether you want to allow for different >>>>> group residual variances. If not, interaction. If so, then I guess the >>>>> easiest approach would be -suest-. >>>>> >>>>> On Mon, Feb 18, 2013 at 11:15 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>>>> Dear Statalisters, >>>>>> >>>>>> I have tryed to solve the question below, searching for help in the >>>>>> Stata Archiv without too much success... >>>>>> >>>>>> I have estimated a fixed effects linear regression for two different >>>>>> groups on my sample (say, sex male/female), using this strategy: >>>>>> xtreg dv iv, if sex==male >>>>>> xtreg dv iv, if sex==female >>>>>> >>>>>> I am interested in testing whether or not the coefficient b1 is >>>>>> identical to each other in the two subsamples. >>>>>> >>>>>> I would really appreciate any help. >>>>>> Regards >>>>>> MJ >>>>>> * >>>>>> * For searches and help try: >>>>>> * http://www.stata.com/help.cgi?search >>>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> >>>>> >>>>> >>>>> -- >>>>> JVVerkuilen, PhD >>>>> jvverkuilen@gmail.com >>>>> >>>>> http://lesswrong.com/ >>>>> >>>>> "Everybody loves progress but nobody likes change." ---Fortune cookie, 1/13/13. >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>> * http://www.ats.ucla.edu/stat/stata/ >>>> >>>> 2013/2/18 JVerkuilen (Gmail) <jvverkuilen@gmail.com>: >>>>> As someone else indicated, your syntax is odd. >>>>> >>>>> The main question I have is whether you want to allow for different >>>>> group residual variances. If not, interaction. If so, then I guess the >>>>> easiest approach would be -suest-. >>>>> >>>>> On Mon, Feb 18, 2013 at 11:15 AM, Mario Jose <mariojose276@gmail.com> wrote: >>>>>> Dear Statalisters, >>>>>> >>>>>> I have tryed to solve the question below, searching for help in the >>>>>> Stata Archiv without too much success... >>>>>> >>>>>> I have estimated a fixed effects linear regression for two different >>>>>> groups on my sample (say, sex male/female), using this strategy: >>>>>> xtreg dv iv, if sex==male >>>>>> xtreg dv iv, if sex==female >>>>>> >>>>>> I am interested in testing whether or not the coefficient b1 is >>>>>> identical to each other in the two subsamples. >>>>>> >>>>>> I would really appreciate any help. >>>>>> Regards >>>>>> MJ >>>>>> * >>>>>> * For searches and help try: >>>>>> * http://www.stata.com/help.cgi?search >>>>>> * http://www.stata.com/support/faqs/resources/statalist-faq/ >>>>>> * http://www.ats.ucla.edu/stat/stata/ >>>>> >>>>> >>>>> >>>>> -- >>>>> JVVerkuilen, PhD >>>>> jvverkuilen@gmail.com >>>>> >>>>> http://lesswrong.com/ >>>>> >>>>> "Everybody loves progress but nobody likes change." ---Fortune cookie, 1/13/13. >>>>> * >>>>> * For searches and help try: >>>>> * http://www.stata.com/help.cgi?search >>>>> * http://www.stata.com/support/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/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/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: comparing equality of coefficients from two subsamples***From:*Mario Jose <mariojose276@gmail.com>

**Re: st: comparing equality of coefficients from two subsamples***From:*"JVerkuilen (Gmail)" <jvverkuilen@gmail.com>

**Re: st: comparing equality of coefficients from two subsamples***From:*Mario Jose <mariojose276@gmail.com>

**Re: st: comparing equality of coefficients from two subsamples***From:*Rebecca Pope <rebecca.a.pope@gmail.com>

**Re: st: comparing equality of coefficients from two subsamples***From:*Mario Jose <mariojose276@gmail.com>

**Re: st: comparing equality of coefficients from two subsamples***From:*Rebecca Pope <rebecca.a.pope@gmail.com>

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