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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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