Re: st: RE: FGLS vs. OLS

[Lucas <lucaselastic@gmail.com>]

I can only look at the stata documentation to see what they say
-sureg- is using.  I know of no way to reverse engineer the command to
see assess whether the documentation is accurate or not.

However, I would say that getting the same answer with -regress- and
-sureg- does NOT mean that -sureg- is using OLS.  FGLS gives the same
coefficients as OLS if the same variables are used in the multiple
equations one uses in -sureg-.

I should note, also, that you have only 1 equation in your
-sureg-command.  -Sureg- is for Zellner's model, and Zellner's model
is for multiple dependent variables and multiple sets of (likely
different) X (i.e., independent) variables.  I do not know the -sureg-
command enough to know what happens if you use it with only 1
dependent variable--perhaps it presumes you made a mistake, and
reverts to OLS.

Stata staff or other users may know what happens in such a case, but I
do not.  Sorry.

Sam

On Tue, Jul 16, 2013 at 11:09 AM, Jordan Silberman
<silberman.stata@gmail.com> wrote:
> This makes sense. But my question is--if sureg uses FGLS, and if FGLS
> yields coefficient estimates that differ from those of OLS, then why
> do the sureg and regress commands (which should be using FGLS and OLS,
> respectively) yield the exact same regression coefficients? Is sureg
> perhaps not really using FGLS?
>
> On Tue, Jul 16, 2013 at 1:57 PM, Lucas <lucaselastic@gmail.com> wrote:
>> It is my understanding that Seemingly Unrelated Regression gives the
>> same results as one-by-one OLS estimation unless one has different X's
>> in the equations.  Even if the X's are the same in all equations, SUR
>> can still be useful because SUR allows appropriate tests of
>> coefficients across equations (because SUR allows coefficients to have
>> non-zero covariances, which are needed to appropriately test them
>> across equations).
>>
>> Sam
>>
>> On Tue, Jul 16, 2013 at 9:31 AM, Jordan Silberman
>> <silberman.stata@gmail.com> wrote:
>>> Thanks Dr. Reed. Stata documentation states that the sureg (seemingly
>>> unrelated regression) command uses FGLS. Therefore, it seems to me
>>> that one should be able to use FGLS to estimate a simple model in
>>> which x predicts y with the following command:
>>>
>>> sureg (y x)
>>>
>>> If sureg uses FGLS, and if the FGLS coefficients are different from
>>> those of OLS, then you'd expect the command above to yield
>>> coefficients that differ from those of a simple OLS regression.
>>> However, when I use a command like "regress y x" to estimate the same
>>> model with OLS, I get the exact same coefficients (standard errors/p
>>> values differ). Why am I getting identical coefficients here, if the 2
>>> commands use 2 different estimators that should yield different
>>> coefficients?
>>>
>>> Thanks,
>>> Jordan
>>>
>>> On Tue, Jul 16, 2013 at 12:03 PM, Bob Reed <bob.reed@canterbury.ac.nz> wrote:
>>>> Hi Jordan,
>>>>
>>>> OLS and GLS estimators will produce different estimates.  The formulae are different, as you can check by referring to most econometrics textbooks.
>>>>
>>>> W. Robert Reed
>>>> Professor
>>>> Department of Economics and Finance
>>>> University of Canterbury
>>>> Private Bag 4800
>>>> Christchurch
>>>> New Zealand
>>>> Phone: +64-3-3642846
>>>> Fax: +64-3-3642635
>>>> Email: bobreednz@yahoo.com
>>>> Homepage: http://www.econ.canterbury.ac.nz/personal_pages/bob_reed/
>>>>
>>>> Replications Co-Editor, Public Finance Review
>>>> http://www.sagepub.com/journalsProdEditBoards.nav?prodId=Journal200768
>>>>
>>>> Editor, ISRN Economics
>>>> http://www.isrn.com/journals/economics/editors/
>>>>
>>>> ________________________________________
>>>> From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Jordan Silberman [silberman.stata@gmail.com]
>>>> Sent: Wednesday, 17 July 2013 3:50 a.m.
>>>> To: statalist@hsphsun2.harvard.edu
>>>> Subject: st: FGLS vs. OLS
>>>>
>>>> Can anyone tell me if it's correct that coefficients computed from an
>>>> OLS regression should be equal to those computed from feasible
>>>> generalized least squares (FGLS) estimation, while standard errors and
>>>> p values should differ across the 2 methods? I'm interested in
>>>> comparing a single linear model across the 2 methods, so there's no
>>>> "seemingly unrelated regression." Thanks, Jordan
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