<ol>
<li><p>You don't <em>need</em> to use sureg. The benefit of SURE is smaller standard errors, but you could just estimate separate regressions. This is what I would advise. Are the independent variables the same in each regression? If so, then there is no efficiency gain in SURE at all, and separate regressions is what you should use.</p>
</li>
<li><p>Your syntax for sureg seems incorrect, you want: sureg (depvar1 varlist1) (depvar2 varlist2) . . . (depvarN varlistN ).</p>
</li>
<li><p>You could replace missing with 0, that's imperfect, but something to think about.</p>
</li>
</ol>

CC BY-SA 4.0
2021-07-31T02:11:24.443Z
Thank you Michael! 1. But I have been advised to use SURE as our error terms are correlated. The independent variables are the same. But if we run separate regressions, then number of observations change, depending on the number of NAs in each independent variable column. 2. When the list of X's is the same, we can use the short notation (https://www.stata.com/manuals/rsureg.pdf). Nonetheless, I also used the one that you mentioned to double check. 3. Wouldn't replacing with 0s be misleading as the party did not contest election from that particular area, and give us biased results?
Anisha Garg
CC BY-SA 4.0
2021-07-31T02:15:47.293Z
Do you know of any literature wherein people did something like this?
Anisha Garg
CC BY-SA 4.0
2021-07-31T12:24:03.150Z
"The independent variables are the same. "
Then SURE and OLS give the same standard errors. This has been discussed on this board. https://economics.stackexchange.com/questions/45753/seemingly-unrelated-regression-estimation-equivalent-to-ols-standard-errors
Michael Gmeiner
CC BY-SA 4.0
2021-07-31T15:02:45.220Z
Thank you for sharing this! :)
Anisha Garg
CC BY-SA 4.0
2021-08-01T13:57:58.187Z
However, my OLS and SURE estimates are coming out to be different
Anisha Garg
CC BY-SA 4.0
2021-08-01T17:08:55.343Z
I can't know for sure without seeing the output, but it may be that SURE is dropping observations from all equations if any of the equations has a missing outcome. Is the number of observations the same when using SURE and OLS?
Michael Gmeiner
CC BY-SA 4.0
2021-08-02T07:03:41.207Z
Yes, there are missing observations, but the number of observations for which I am getting different results is the same in SURE and OLS. Can we connect on personal chat? I would be able to better explain my problem. Thanks a lot!
Anisha Garg
CC BY-SA 4.0
2021-08-02T11:19:59.617Z
I don't want to put email here, but you can find me on LinkedIn or Twitter ("Gmeiner" is a unique name, there aren't many of me).  We can possibly zoom and you can share screen of your Stata output.
Michael Gmeiner
CC BY-SA 4.0
2021-08-02T11:33:30.193Z
Also, if the results are not very different, it is likely because in STATA, the reg command of OLS performs a degrees of freedom adjustment for standard errors, while the sureg command does not, so they will report slightly different estimates even though the theoretic properties are the same.
Michael Gmeiner
CC BY-SA 4.0
2021-08-03T05:21:58.957Z
I followed you on Twitter but am not able to dm you. So, I have sent you a connection request on LinkedIn. Having a zoom call would be great.:)
Anisha Garg
CC BY-SA 4.0
2021-07-30T17:46:51.983Z
2021-07-30T17:46:51.983Z
Michael Gmeiner
<p>I am trying to estimate vote shares of different parties. So, suppose I have 4 parties, each having its own column in the data set. Hence, the sum would be tending to 1. Now, if a party does not contest in a particular area, then the vote share for that area is '.'.
So, I have 4 equations -</p>
<pre><code>sureg (voteshare_party1 voteshare_party2 voteshare_party3 vote share_party4 = X_variables i.fixedeffectvariable1 i.fixedeffectvariable2)
</code></pre>
<p>What I want to do is remove NAs separately for each equation, like it would have happened with <code>reghdfe</code>. But I need to use <code>sureg</code> because the error terms are correlated. Do you have any suggestions for this? Or any research paper which did something similar?</p>
<p>I am using Stata. In R, I used systemfit() but the estimates I am getting from Stata and R vary widely. I am happy to use any method given my estimates are consistent.</p>
<p>I really appreciate any <strong>suggestions / literature</strong> on this. And am happy to share more context.</p>

<econometrics><regression><paneldata><stata><r>
How to estimate a system of equations with missing values in dependent variable with sureg?
Anisha Garg
# How to estimate a system of equations with missing values in dependent variable with sureg?

(Asked by: username_0 on 2021-07-30)

I am trying to estimate vote shares of different parties. So, suppose I have 4 parties, each having its own column in the data set. Hence, the sum would be tending to 1. Now, if a party does not contest in a particular area, then the vote share for that area is '.'.
So, I have 4 equations -

```
sureg (voteshare_party1 voteshare_party2 voteshare_party3 vote share_party4 = X_variables i.fixedeffectvariable1 i.fixedeffectvariable2)

```

What I want to do is remove NAs separately for each equation, like it would have happened with `reghdfe`. But I need to use `sureg` because the error terms are correlated. Do you have any suggestions for this? Or any research paper which did something similar?

I am using Stata. In R, I used systemfit() but the estimates I am getting from Stata and R vary widely. I am happy to use any method given my estimates are consistent.

I really appreciate any **suggestions / literature** on this. And am happy to share more context.

**Tags:** econometrics, regression, paneldata, stata, r


---

## 1 answers

### Answer by username_1 on 2021-07-30. Score: 1 | Accepted answer

1. You don't *need* to use sureg. The benefit of SURE is smaller standard errors, but you could just estimate separate regressions. This is what I would advise. Are the independent variables the same in each regression? If so, then there is no efficiency gain in SURE at all, and separate regressions is what you should use.
2. Your syntax for sureg seems incorrect, you want: sureg (depvar1 varlist1) (depvar2 varlist2) . . . (depvarN varlistN ).
3. You could replace missing with 0, that's imperfect, but something to think about.

> **Comments:**
> **username_0**: Thank you username_1! 1. But I have been advised to use SURE as our error terms are correlated. The independent variables are the same. But if we run separate regressions, then number of observations change, depending on the number of NAs in each independent variable column. 2. When the list of X's is the same, we can use the short notation (https://www.stata.com/manuals/rsureg.pdf). Nonetheless, I also used the one that you mentioned to double check. 3. Wouldn't replacing with 0s be misleading as the party did not contest election from that particular area, and give us biased results?  
> **username_0**: Do you know of any literature wherein people did something like this?  
> **username_1**: "The independent variables are the same. " Then SURE and OLS give the same standard errors. This has been discussed on this board. https://economics.stackexchange.com/questions/45753/seemingly-unrelated-regression-estimation-equivalent-to-ols-standard-errors  
> **username_0**: Thank you for sharing this! :)  
> **username_0**: However, my OLS and SURE estimates are coming out to be different  
> **username_1**: I can't know for sure without seeing the output, but it may be that SURE is dropping observations from all equations if any of the equations has a missing outcome. Is the number of observations the same when using SURE and OLS?  
> **username_0**: Yes, there are missing observations, but the number of observations for which I am getting different results is the same in SURE and OLS. Can we connect on personal chat? I would be able to better explain my problem. Thanks a lot!  
> **username_1**: I don't want to put email here, but you can find me on LinkedIn or Twitter ("username_1" is a unique name, there aren't many of me).  We can possibly zoom and you can share screen of your Stata output.  
> **username_1**: Also, if the results are not very different, it is likely because in STATA, the reg command of OLS performs a degrees of freedom adjustment for standard errors, while the sureg command does not, so they will report slightly different estimates even though the theoretic properties are the same.  
> **username_0**: I followed you on Twitter but am not able to dm you. So, I have sent you a connection request on LinkedIn. Having a zoom call would be great.:)  

---

