Stata The Stata listserver
[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

st: RE: RE: Re: fixed effects and SUR


From   "J. Yimin Zhou" <zymx@yahoo.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: Re: fixed effects and SUR
Date   Wed, 23 Oct 2002 13:29:47 +0900

Dear Kit,

I posted the original question. I appreciated all of those responses that
question generated and benefited from the ensuing discussions (even though I
may or may not agree with all of them). Your example program gave me some
idea how I could proceed with what I intended to do with Stata. (As a new
user, I've got a lot to learn from you guys.) Therefore, the purpose of my
question is well served. Thank you.

Nik's question as well as my original one, however, is legitimate. You may
think that "sureg" is a panel data estimator, but it does not handle fixed
effects by itself. Your example clearly indicated that you had to combine
"sureg" with "xtreg" to deal with the issue at hand, which was exactly what
I didn't but wanted to know about when I raised my question. Therefore, your
claim that by using "sureg," one is already estimating fixed effects is, I
hate to say, less than true.

Thank you again for your generous discussions and example program, even
though I wish that you had shared this example program with me right after I
posted my question.

Jeremy Z.

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of baum
Sent: Wednesday, October 23, 2002 12:30 AM
To: Nick Winter; StataList
Subject: st: RE: Re: fixed effects and SUR


Nick,

If you take a panel and reshape wide by i, you can estimate the same model
on sureg, right? In my mind sureg is a panel data estimator.


. use http://fmwww.bc.edu/ec-p/data/Greene2000/TBL15-1

. tsset firm year
       panel variable:  firm, 1 to 5
        time variable:  year, 1935 to 1954


. xtreg i f c,fe

Fixed-effects (within) regression               Number of obs      =
100
Group variable (i) : firm                       Number of groups   =
5

R-sq:  within  = 0.8003                         Obs per group: min =
20
       between = 0.7699                                        avg =
20.0
       overall = 0.7782                                        max =
20

                                                F(2,93)            =
186.40
corr(u_i, Xb)  = -0.1359                        Prob > F           =
0.0000

---------------------------------------------------------------------------
---
           i |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
Interval]
-------------+-------------------------------------------------------------
---
           f |   .1059799    .015891     6.67   0.000     .0744236
.1375363
           c |   .3466596   .0241612    14.35   0.000     .2986803
.3946388
       _cons |  -62.59438   29.44191    -2.13   0.036    -121.0602
-4.128578
-------------+-------------------------------------------------------------
---
     sigma_u |  120.02194
     sigma_e |  69.117977
         rho |  .75095637   (fraction of variance due to u_i)
---------------------------------------------------------------------------
---
F test that all u_i=0:     F(4, 93) =    58.96               Prob > F =
0.0000



. reshape wide i f c,i(year) j(firm)
(note: j = 1 2 3 4 5)

Data                               long   ->   wide
---------------------------------------------------------------------------
--
Number of obs.                      100   ->      20
Number of variables                   5   ->      16
j variable (5 values)              firm   ->   (dropped)
xij variables:
                                      i   ->   i1 i2 ... i5
                                      f   ->   f1 f2 ... f5
                                      c   ->   c1 c2 ... c5
---------------------------------------------------------------------------
--

. forv i=1/5 {
  2. local rhs "`rhs' ( i`i' f`i' c`i') "
  3. }

. di "`rhs'"
 ( i1 f1 c1)  ( i2 f2 c2)  ( i3 f3 c3)  ( i4 f4 c4)  ( i5 f5 c5)

. sureg `rhs'

Seemingly unrelated regression
----------------------------------------------------------------------
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
----------------------------------------------------------------------
i1                 20      2    84.94729    0.9207   261.3219   0.0000
i2                 20      2    12.36322    0.9119   207.2128   0.0000
i3                 20      2    26.46612    0.6876   46.88498   0.0000
i4                 20      2    9.742303    0.7264   59.14585   0.0000
i5                 20      2    95.85484    0.4220    14.9687   0.0006
----------------------------------------------------------------------

---------------------------------------------------------------------------
---
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+-------------------------------------------------------------
---
i1           |
          f1 |    .120493   .0216291     5.57   0.000     .0781007
.1628853
          c1 |   .3827462    .032768    11.68   0.000      .318522
.4469703
       _cons |  -162.3641   89.45922    -1.81   0.070    -337.7009
12.97279
-------------+-------------------------------------------------------------
---
i2           |
          f2 |   .0695456   .0168975     4.12   0.000     .0364271
.1026641
          c2 |   .3085445   .0258635    11.93   0.000     .2578529
.3592362
       _cons |   .5043112   11.51283     0.04   0.965    -22.06042
23.06904
-------------+-------------------------------------------------------------
---
i3           |
          f3 |   .0372914   .0122631     3.04   0.002     .0132561
.0613268
          c3 |    .130783   .0220497     5.93   0.000     .0875663
.1739997
       _cons |  -22.43892   25.51859    -0.88   0.379    -72.45443
27.57659
-------------+-------------------------------------------------------------
---
i4           |
          f4 |   .0570091   .0113623     5.02   0.000     .0347395
.0792788
          c4 |   .0415065   .0412016     1.01   0.314    -.0392472
.1222602
       _cons |   1.088878   6.258805     0.17   0.862    -11.17815
13.35591
-------------+-------------------------------------------------------------
---
i5           |
          f5 |   .1014782   .0547837     1.85   0.064    -.0058958
.2088523
          c5 |   .3999914   .1277946     3.13   0.002     .1495186
.6504642
       _cons |   85.42324   111.8774     0.76   0.445    -133.8525
304.6989
---------------------------------------------------------------------------
---


If you impose constraints that the slopes are common across equations of
the SUR, you're going to be very close to a xtreg,fe model.

Kit



--On Tuesday, October 22, 2002 11:10 -0400 Nick Winter
<nwinter@policystudies.com> wrote:

> Kit,
>
> I'm confused by your mentioning of -sureg- in relation to panel models.
> How does sureg estimate what you describe?  I thought of -sureg- as
> estimating different models on the same cases; not the same models on
> different sets of cases?
>
> But maybe I'm just missing something obvious?
>
> Thanks
> Nick
>
>
> -----------------------------------------------------------
>  Nicholas Winter, Ph.D.                     P 202.939.5343
>  Policy Studies Associates                  F 202.939.5732
>  1718 Connecticut Avenue, NW     nwinter@policystudies.com
>  Washington, DC 20009-1148           www.policystudies.com
> -----------------------------------------------------------
>
>> -----Original Message-----
>> From: Kit Baum, Faculty Micro Resource Center [mailto:fmrc@bc.edu]
>> Sent: Tuesday, October 22, 2002 10:57 AM
>> To: statalist@hsphsun2.harvard.edu
>> Subject: st: Re: fixed effects and SUR
>>
>>
>>
>> --On Tuesday, October 22, 2002 2:33 -0400 Jeremy wrote:
>>
>> >
>> > Hi Everybody,
>> >
>> > Could anybody give me some idea how to estimate SUR with
>> fixed effects
>> > using Stata? I'm new to Stata. All I know at this point is
>> that I could
>> > use XTREG to estimate a single equation with fixed effects,
>> and SUREG to
>> > estimate a system of equations. I've no idea how to proceed
>> from here.
>> > For your information, I'm trying to use Stata to "cross-check" some
>> > estimation results I obtained using TSP. Your answers will
>> be gratefully
>> > appreciated.
>> >
>> > Jeremy Z.
>> >
>> and cb23 responded
>> >
>> > Just in case no-one comes up with a correct answer on this,
>> I would try
>> > the following :-
>> >
>> > I think I am right in saying that the xtreg fixed effects
>> model is just
>> > a standard OLS model with dummies for the groups with one
>> alteration: in
>> > OLS we choose a baseline dummy for which we set the
>> coefficient to zero
>> > and in fixed effects we sum the dummy coefficients on all groups to
>> > zero.  This means that the coefficients on the other Xs
>> should be the
>> > same in fixed effects and the dummy variable approach, and the only
>> > difference will be in the coefficients of the constant and the group
>> > dummies/effects.  You should try this to make sure it
>> works.  You can
>> > even try to choose the baseline dummy such that the
>> coefficients on the
>> > other dummies are close to summing to zero.
>> >
>> > Than, having realised we can roughly write a fixed effect model as a
>> > standard equation, you could then rewrite your fixed effect
>> equations in
>> > a dummy variable form and stick them in to a SUR model.
>>
>>
>> I find this quite confused. Note that if we start with the
>> most general
>> (infeasible) model of panel data, in which every i and t has its own
>> coefficient vector, we can define special cases:
>>
>> a) all slopes constant over i and t, s^2 constant over i and
>> t, intercept
>> varies over i
>>
>> b) intercept, slopes, and s^2 all have an i subscript, but
>> are constant
>> over t
>>
>> The former case is one-way (individual) fixed effects, aka
>> LSDV (dummy var)
>> model, which may be estimated by xtreg, fe or areg. Note that
>> normalisation
>> of the intercepts makes no difference here; no matter whether
>> you include a
>> constant and (n-1) dummies, or express data as demeaned by
>> individual, you
>> will get the same estimates in terms of significance.
>>
>> The latter case is Zellner SUR, estimable via sureg. This is a 'fixed
>> effect' model, in that each individual has his/her own
>> equation (thus N < T
>> for standard SUR), with his/her own intercept, set of slopes,
>> and s^2. One
>> can consider special cases of SUR in which further
>> constraints are imposed
>> (e.g. common slopes over units) which, since SUR is a GLS
>> estimator, takes
>> you back very close to individual fixed effects (except that
>> SUR allows for
>> s^2_i, whereas
>> IFE imposes a single s^2 on the entire panel).
>>
>> So I don't know what it means to estimate SUR with fixed
>> effects; if you're
>> using SUR, you are already estimating individual fixed
>> effects, and more.
>>
>> Kit
>> --------------------------------------------------------------------
>> Kit Baum, Faculty Micro Resource Center                  fmrc@bc.edu
>> Academic Technology Services, Boston College   http://www.bc.edu/ats
>> http://fmwww.bc.edu/FMRC/                http://fmwww.bc.edu/GStat/
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/support/faqs/res/findit.html
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>


*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/

*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/



© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   What's new   |   Site index