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Re: st: SUREG with if command.


From   David Ashcraft <[email protected]>
To   "[email protected]" <[email protected]>
Subject   Re: st: SUREG with if command.
Date   Tue, 25 Sep 2012 09:21:44 -0700 (PDT)

Thanks Nick, by saving number of observations, I meant e.g. for rp1, I have 120 observations so I want -sureg- to utilize 120 observations not 60 observations.  Is there a way, I could utilize all non-missing observations for each equation in -sureg- model?   

edit rp? if dummy==1: two variable rp8 and rp9 have all observations as missing. I dropped these two equations from the model for dummy==1. I have got some results and these are inline with my expectations. I also have checked for observation where dummy==0 and have found several instances of missing observations. 
Regards

David

----- Original Message -----
From: Nick Cox <[email protected]>
To: [email protected]
Cc: 
Sent: Tuesday, September 25, 2012 6:34:03 PM
Subject: Re: st: SUREG with if command.

I've never used -sureg-. It seems to me that it uses or knows nothing
about panel structure, so I surmise that it is indifferent to
unbalanced panels as such. But it seems that you do have missing
values in different observations and will have problems because
-sureg- can only function with non-missing values on all variables
named.

Look at

edit rp? if dummy == 1

I don't know what you mean by "save the number of observation[s]".

Nick

On Tue, Sep 25, 2012 at 4:21 PM, David Ashcraft
<[email protected]> wrote:
> Hello Nick,
>
> I think problem is not with the dummy variable. This may be related -sureg- model. It seems to me -sureg- needs a balanced panel resulting in drop of number of observations considerably while implementing -sureg-.
>
> Is there any other way where I can save the number of observation and still use seemingly unrelated regression model?
>
> Below is some descriptive stats for your review.
>
>
> gen dummy=0
> . replace dummy = 1 if date2>17532
>
> (53 real changes made)
> . tabulate dummy
>
>
>
>       dummy |      Freq.     Percent        Cum.
> ------------+-----------------------------------
>           0 |         96       64.43       64.43
>           1 |         53       35.57      100.00
> ------------+-----------------------------------
>       Total |        149      100.00
>
> . su rp1 rp2 rp3 rp4 rp6 rp7 rp8 rp9
>
>     Variable |       Obs        Mean    Std. Dev.       Min        Max
> -------------+--------------------------------------------------------
>          rp1 |       120     .001517    .0469446  -.1935012   .1102614
>          rp2 |       120    .0008538    .0545707   -.212302   .1238899
>          rp3 |       120    .0016796    .0565703  -.2283529   .1202257
>          rp4 |       120    .0016847    .0588602  -.2037239   .1229283
>          rp6 |       120    .0015542     .056026  -.2226954   .1190248
> -------------+--------------------------------------------------------
>          rp7 |       120    .0016078    .0503465  -.2033414   .1073936
>          rp8 |        88    .0023456    .0709356  -.2216449   .1371796
>          rp9 |        88    .0033193    .0779086  -.2401649   .1579783
>
>
>
>
>
> ----- Original Message -----
> From: Nick Cox <[email protected]>
> To: [email protected]
> Cc:
> Sent: Tuesday, September 25, 2012 2:54:44 PM
> Subject: Re: st: SUREG with if command.
>
> Your results show 60 observations with non-missing values; dummy is 0
> on 60 of them (all) and so necessarily 1 on 0 (none) of them. Stata's
> response is reasonable; there are _no_ observations to do your last
> command.
>
> You should perhaps revisit your definition of -dummy-, which doesn't
> divide the dataset.
>
> Nick
>
> On Tue, Sep 25, 2012 at 12:42 PM, David Ashcraft
> <[email protected]> wrote:
>> Hi Statalist,
>>
>> I am trying to run -sureg- with multiple equation as per below. Now I have divided my data based on dummy variable and I want to see if the results are any different based on the dummy variable. Based on my data, I should get three different results i.e. one for the whole sample and two based on the dummy variable. I am getting the same result for the overall sample and where dummy==0 and getting no result for dummy==1.
>>
>> I don't understand why I am getting this result. Can anyone help me direct to the solution of this problem. I have looked at some older posts but there is no answer.
>> Regards
>>
>> David
>>
>>
>>
>> sureg (rp1 rm1)(rp2 rm2)(rp3 rm3)(rp4 rm4)(rp6 rm6)(rp7 rm7)(rp8 rm8)(rp9 rm9), corr
>>
>> Seemingly unrelated regression
>> ----------------------------------------------------------------------
>> Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
>> ----------------------------------------------------------------------
>> rp1                60      1    .0071761    0.9616    3298.70   0.0000
>> rp2                60      1    .0092113    0.9465    2534.55   0.0000
>> rp3                60      1    .0082266    0.9544    2847.07   0.0000
>> rp4                60      1    .0091633    0.9491    2198.62   0.0000
>> rp6                60      1    .0084368    0.9515    2677.13   0.0000
>> rp7                60      1    .0060539    0.9711    3703.34   0.0000
>> rp8                60      1     .009352    0.9866    5504.52   0.0000
>> rp9                60      1    .0115533    0.9832    4137.04   0.0000
>> ----------------------------------------------------------------------
>>
>> ------------------------------------------------------------------------------
>>              |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> rp1          |
>>          rm1 |   .9992066   .0173974    57.43   0.000     .9651084    1.033305
>>        _cons |  -.0000674   .0009287    -0.07   0.942    -.0018876    .0017527
>> -------------+----------------------------------------------------------------
>> rp2          |
>>          rm2 |   .9733916   .0193347    50.34   0.000     .9354963    1.011287
>>        _cons |  -.0013549    .001196    -1.13   0.257     -.003699    .0009892
>> -------------+----------------------------------------------------------------
>> rp3          |
>>          rm3 |   .9942406   .0186334    53.36   0.000     .9577198    1.030761
>>        _cons |  -.0008887   .0010727    -0.83   0.407    -.0029911    .0012136
>> -------------+----------------------------------------------------------------
>> rp4          |
>>          rm4 |   .9618481   .0205131    46.89   0.000     .9216431    1.002053
>>        _cons |   .0010966   .0011632     0.94   0.346    -.0011832    .0033765
>> -------------+----------------------------------------------------------------
>> rp6          |
>>          rm6 |   .9920405   .0191732    51.74   0.000     .9544617    1.029619
>>        _cons |  -.0008398    .001099    -0.76   0.445    -.0029937    .0013142
>> -------------+----------------------------------------------------------------
>> rp7          |
>>          rm7 |   .9873097    .016224    60.86   0.000     .9555113    1.019108
>>        _cons |   .0001045   .0007886     0.13   0.895    -.0014411    .0016502
>> -------------+----------------------------------------------------------------
>> rp8          |
>>          rm8 |   .9554066   .0128774    74.19   0.000     .9301673    .9806458
>>        _cons |   .0009302   .0012033     0.77   0.439    -.0014282    .0032887
>> -------------+----------------------------------------------------------------
>> rp9          |
>>          rm9 |   .9889603   .0153757    64.32   0.000     .9588245    1.019096
>>        _cons |  -.0001408   .0014852    -0.09   0.924    -.0030518    .0027703
>> ------------------------------------------------------------------------------
>>
>> Correlation matrix of residuals:
>>
>>          rp1      rp2      rp3      rp4      rp6      rp7      rp8      rp9
>> rp1   1.0000
>> rp2   0.1214   1.0000
>> rp3   0.2210   0.9609   1.0000
>> rp4   0.4345  -0.0342   0.0495   1.0000
>> rp6   0.2268   0.9595   0.9982   0.0447   1.0000
>> rp7   0.9088   0.2710   0.3749   0.7114   0.3763   1.0000
>> rp8   0.2240   0.0839   0.0896  -0.0739   0.0987   0.1232   1.0000
>> rp9   0.2100   0.1163   0.1462  -0.0321   0.1497   0.1600   0.7653   1.0000
>>
>> Breusch-Pagan test of independence: chi2(28) =   338.778, Pr = 0.0000
>>
>> . sureg (rp1 rm1)(rp2 rm2)(rp3 rm3)(rp4 rm4)(rp6 rm6)(rp7 rm7)(rp8 rm8)(rp9 rm9)if dummy==0, corr
>>
>> Seemingly unrelated regression
>> ----------------------------------------------------------------------
>> Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
>> ----------------------------------------------------------------------
>> rp1                60      1    .0071761    0.9616    3298.70   0.0000
>> rp2                60      1    .0092113    0.9465    2534.55   0.0000
>> rp3                60      1    .0082266    0.9544    2847.07   0.0000
>> rp4                60      1    .0091633    0.9491    2198.62   0.0000
>> rp6                60      1    .0084368    0.9515    2677.13   0.0000
>> rp7                60      1    .0060539    0.9711    3703.34   0.0000
>> rp8                60      1     .009352    0.9866    5504.52   0.0000
>> rp9                60      1    .0115533    0.9832    4137.04   0.0000
>> ----------------------------------------------------------------------
>>
>> ------------------------------------------------------------------------------
>>              |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>> rp1          |
>>          rm1 |   .9992066   .0173974    57.43   0.000     .9651084    1.033305
>>        _cons |  -.0000674   .0009287    -0.07   0.942    -.0018876    .0017527
>> -------------+----------------------------------------------------------------
>> rp2          |
>>          rm2 |   .9733916   .0193347    50.34   0.000     .9354963    1.011287
>>        _cons |  -.0013549    .001196    -1.13   0.257     -.003699    .0009892
>> -------------+----------------------------------------------------------------
>> rp3          |
>>          rm3 |   .9942406   .0186334    53.36   0.000     .9577198    1.030761
>>        _cons |  -.0008887   .0010727    -0.83   0.407    -.0029911    .0012136
>> -------------+----------------------------------------------------------------
>> rp4          |
>>          rm4 |   .9618481   .0205131    46.89   0.000     .9216431    1.002053
>>        _cons |   .0010966   .0011632     0.94   0.346    -.0011832    .0033765
>> -------------+----------------------------------------------------------------
>> rp6          |
>>          rm6 |   .9920405   .0191732    51.74   0.000     .9544617    1.029619
>>        _cons |  -.0008398    .001099    -0.76   0.445    -.0029937    .0013142
>> -------------+----------------------------------------------------------------
>> rp7          |
>>          rm7 |   .9873097    .016224    60.86   0.000     .9555113    1.019108
>>        _cons |   .0001045   .0007886     0.13   0.895    -.0014411    .0016502
>> -------------+----------------------------------------------------------------
>> rp8          |
>>          rm8 |   .9554066   .0128774    74.19   0.000     .9301673    .9806458
>>        _cons |   .0009302   .0012033     0.77   0.439    -.0014282    .0032887
>> -------------+----------------------------------------------------------------
>> rp9          |
>>          rm9 |   .9889603   .0153757    64.32   0.000     .9588245    1.019096
>>        _cons |  -.0001408   .0014852    -0.09   0.924    -.0030518    .0027703
>> ------------------------------------------------------------------------------
>>
>> Correlation matrix of residuals:
>>
>>          rp1      rp2      rp3      rp4      rp6      rp7      rp8      rp9
>> rp1   1.0000
>> rp2   0.1214   1.0000
>> rp3   0.2210   0.9609   1.0000
>> rp4   0.4345  -0.0342   0.0495   1.0000
>> rp6   0.2268   0.9595   0.9982   0.0447   1.0000
>> rp7   0.9088   0.2710   0.3749   0.7114   0.3763   1.0000
>> rp8   0.2240   0.0839   0.0896  -0.0739   0.0987   0.1232   1.0000
>> rp9   0.2100   0.1163   0.1462  -0.0321   0.1497   0.1600   0.7653   1.0000
>>
>> Breusch-Pagan test of independence: chi2(28) =   338.778, Pr = 0.0000
>>
>> . sureg (rp1 rm1)(rp2 rm2)(rp3 rm3)(rp4 rm4)(rp6 rm6)(rp7 rm7)(rp8 rm8)(rp9 rm9)if dummy==1, corr
>> insufficient observations
>> r(2001);

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