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Re: st: SUREG with if command.
From
Nick Cox <[email protected]>
To
[email protected]
Subject
Re: st: SUREG with if command.
Date
Tue, 25 Sep 2012 17:31:47 +0100
-sureg- will always do the best it can, and there is no extra trickery
except by imputing missing values.
For clarity, don't think or write in terms of missing observations.
It's values that are missing, not observations. Remember, for Stata an
observation is a complete row, record, or case in other terminology.
Nick
On Tue, Sep 25, 2012 at 5:21 PM, David Ashcraft
<[email protected]> wrote:
> 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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