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Re: Re: st: RE: Can I compare the coefficients of one certain variablefrom two different samples by -suest-?


From   gjhxmu@sina.com
To   statalist<statalist@hsphsun2.harvard.edu>
Subject   Re: Re: st: RE: Can I compare the coefficients of one certain variablefrom two different samples by -suest-?
Date   Sat, 13 Mar 2010 22:55:58 +0800

Dear Mitchell,

Thank you very much! It helps a lot!

Best regards,

Rose.

----- Original Message -----
From: Michael Norman Mitchell <Michael.Norman.Mitchell@gmail.com>
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: RE: Can I compare the coefficients of one certain variablefrom two different samples by -suest-?
Date: 2010-3-13 18:21:45

Dear Rose

You can indeed. Here is a FAQ page that shows how you can do this using 
an interaction approach...

http://www.ats.ucla.edu/stat/stata/faq/compreg3.htm

and here is the same example worked using suest.

http://www.ats.ucla.edu/stat/Stata/code/suest.htm

I would add that I think that "suest" is a very cool command!

Enjoy!

Michael N. Mitchell
See the Stata tidbit of the week at...
http://www.MichaelNormanMitchell.com

On 2010-03-13 1.25 AM, gjhxmu@sina.com wrote:
> Dear Martin,
> Thank you for your previous reply.
> Could you please tell me whether I can compare the coefficients of one certain variable from two different samples by -suest-?
>
> Thank you very much!
>
> Rose.
>
> ----- Original Message -----
> From: Martin Weiss<martin.weiss1@gmx.de>
> To:<statalist@hsphsun2.harvard.edu>
> Subject: st: RE: Can I compare the coefficients of one certain variable from two different samples by -suest-?
> Date: 2010-1-4 03:37:15
>
>
> <>
>
> " If can, why the coeffiecients in -suest- are different from independent
> -reg- no matter whether I take vce(robust) or not ?"
>
> See [R], page 1802, first "technical note"
>
>
> " What does "_lnvar" mean?"
>
>
> See page 1803:
>
> " regress does not include its ancillary parameter, the residual variance,
> in its coefficient vector
> and (co)variance matrix. Moreover, while the score option is allowed with
> predict after regress,
> a score variable is generated for the mean but not for the variance
> parameter. suest contains special
> code that assigns the equation name mean to the coefficients for the mean,
> adds the equation lnvar
> for the log variance, and computes the appropriate two score variables
> itself."
>
>
> HTH
> Martin
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of gjhxmu@sina.com
> Sent: Sonntag, 3. Januar 2010 18:14
> To: statalist
> Subject: st: Can I compare the coefficients of one certain variable from two
> different samples by -suest-?
>
> Dear statalists,
>
> Can I compare the coefficients of one certain variable from two different
> samples by -suest-?
>
> If can, why the coeffiecients in -suest- are different from independent
> -reg- no matter whether I take vce(robust) or not ? Which one should I take
> to report?
>
> What does "_lnvar" mean?
>
>
> webuse income,clear
>
> .
> . regress inc edu exp if male
>
> Source | SS df MS Number of obs =
> 110
> -------------+------------------------------ F( 2, 107) =
> 20.05
> Model | 639.919043 2 319.959521 Prob> F =
> 0.0000
> Residual | 1707.31485 107 15.9562136 R-squared =
> 0.2726
> -------------+------------------------------ Adj R-squared =
> 0.2590
> Total | 2347.2339 109 21.5342559 Root MSE =
> 3.9945
>
> ----------------------------------------------------------------------------
> --
> inc | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> edu | 1.841002 .383369 4.80 0.000 1.081018
> 2.600986
> exp | 1.590727 .3569439 4.46 0.000 .8831278
> 2.298327
> _cons | 1.783822 .3818906 4.67 0.000 1.026769
> 2.540876
> ----------------------------------------------------------------------------
> --
> .
> . estimates store Male
> .
> .
> . regress inc edu exp if !male
>
> Source | SS df MS Number of obs =
> 167
> -------------+------------------------------ F( 2, 164) =
> 43.30
> Model | 1418.47853 2 709.239266 Prob> F =
> 0.0000
> Residual | 2686.09306 164 16.3786162 R-squared =
> 0.3456
> -------------+------------------------------ Adj R-squared =
> 0.3376
> Total | 4104.57159 166 24.7263349 Root MSE =
> 4.0471
>
> ----------------------------------------------------------------------------
> --
> inc | Coef. Std. Err. t P>|t| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> edu | 2.475213 .3160483 7.83 0.000 1.851165
> 3.099261
> exp | 1.354081 .3043211 4.45 0.000 .7531885
> 1.954974
> _cons | 1.250719 .3132966 3.99 0.000 .6321043
> 1.869334
> ----------------------------------------------------------------------------
> --
>
> . . estimates store Female.
> .
> .
> . suest Male Female
>
> Simultaneous results for Male, Female
>
> Number of obs =
> 277
>
> ----------------------------------------------------------------------------
> --
> | Robust
> | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
> -------------+--------------------------------------------------------------
> --
> Male_mean |
> edu | 1.841002 .3911029 4.71 0.000 1.074454
> 2.607549
> exp | 1.590727 .3320187 4.79 0.000 .9399827
> 2.241472
> _cons | 1.783822 .3829948 4.66 0.000 1.033166
> 2.534478
> -------------+--------------------------------------------------------------
> --
> Male_lnvar |
> _cons | 2.769848 .1328349 20.85 0.000 2.509497
> 3.0302
> -------------+--------------------------------------------------------------
> --
> Female_mean |
> edu | 2.475213 .3093986 8.00 0.000 1.868803
> 3.081623
> exp | 1.354081 .2982058 4.54 0.000 .7696084
> 1.938554
> _cons | 1.250719 .3122779 4.01 0.000 .638666
> 1.862773
> -------------+--------------------------------------------------------------
> --
> Female_lnvar |
> _cons | 2.795977 .0976384 28.64 0.000 2.604609
> 2.987344
> ----------------------------------------------------------------------------
> --
>
>
> . test [Male_mean]edu = [Female_mean]edu
>
> ( 1) [Male_mean]edu - [Female_mean]edu = 0
>
> chi2( 1) = 1.62
> Prob> chi2 = 0.2035
>
>
>
> Many thanks for any help!
>
>
>
> Best regards,
>
> Rose.
>
>
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