Statalist The Stata Listserver


[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]

Re: st: Fixed Effect Estimation Results


From   "Joana Quina" <joana.statalist@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Fixed Effect Estimation Results
Date   Tue, 4 Apr 2006 17:13:37 +0100

Dear William,

Firstly, I apologise for not having reported my results.  The ones you
quote are the ones obtained by Sam, who initiated this thread.

My results are shown below.  The corr is very high (-0.9899) and the
within R2 is 0.4587.  There are *no* extra variables in the
random-effects estimation.  A similar model specification has been
used in the literature.

Also, I found that it is the "lpop" variable that is driving the high
correlation result - omitting it surprisingly *reduces* the
correlation.

I have re-run my regressions several times and always obtain the same
results. I regularly check updates (a while ago there was a problem
with xtreg, which was corrected).

Joana

. xtreg lbeda_pc lpop lgdp_pc_ppp elrsacw polity_n pts_s_n corrupt
milm_j us_un_fr
> iend japan_un_friend uk_un_friend france_un_friend  indep, re

Random-effects GLS regression                   Number of obs      =        96
Group variable (i): id                          Number of groups   =        27

R-sq:  within  = 0.4093                         Obs per group: min =         2
       between = 0.6133                                        avg =       3.6
       overall = 0.5911                                        max =         4

Random effects u_i ~ Gaussian                   Wald chi2(12)      =     73.52
corr(u_i, X)       = 0 (assumed)                Prob > chi2        =    0.0000

------------------------------------------------------------------------------
    lbeda_pc |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        lpop |  -.6168131   .1338557    -4.61   0.000    -.8791654   -.3544608
lgdp_pc_ppp |  -.2690165   .1925044    -1.40   0.162    -.6463181    .1082851
     elrsacw |   .1004888   .1544715     0.65   0.515    -.2022697    .4032473
    polity_n |   .0293785   .0180981     1.62   0.105    -.0060931    .0648501
     pts_s_n |   .0405079   .0644439     0.63   0.530    -.0857998    .1668155
     corrupt |  -.1543259   .0422096    -3.66   0.000    -.2370552   -.0715967
      milm_j |  -.0010754   .0014974    -0.72   0.473    -.0040103    .0018594
us_un_friend |  -.0329801   .0127515    -2.59   0.010    -.0579725   -.0079877
japan_un_f~d |   .0455285   .0226189     2.01   0.044     .0011962    .0898608
uk_un_friend |   .0178181   .0290755     0.61   0.540    -.0391689    .0748052
france_un_~d |   .0135801   .0212409     0.64   0.523    -.0280514    .0552116
       indep |  -.0052071    .009476    -0.55   0.583    -.0237797    .0133656
       _cons |   1.744271   2.436455     0.72   0.474    -3.031092    6.519634
-------------+----------------------------------------------------------------
     sigma_u |  .68013164
     sigma_e |  .29411034
         rho |  .84246212   (fraction of variance due to u_i)
------------------------------------------------------------------------------

. est store random2

. xtreg lbeda_pc lpop lgdp_pc_ppp elrsacw polity_n pts_s_n corrupt
milm_j us_un_fr
> iend japan_un_friend uk_un_friend france_un_friend  indep, fe

Fixed-effects (within) regression               Number of obs      =        96
Group variable (i): id                          Number of groups   =        27

R-sq:  within  = 0.4587                         Obs per group: min =         2
       between = 0.5312                                        avg =       3.6
       overall = 0.5104                                        max =         4

                                                F(12,57)           =      4.03
corr(u_i, Xb)  = -0.9899                        Prob > F           =    0.0002

------------------------------------------------------------------------------
    lbeda_pc |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        lpop |  -4.848811    2.22036    -2.18   0.033    -9.295005   -.4026177
lgdp_pc_ppp |  -.2184703   .3257575    -0.67   0.505    -.8707884    .4338478
     elrsacw |   .3184507   .1787963     1.78   0.080    -.0395826    .6764841
    polity_n |   .0598919   .0217173     2.76   0.008     .0164037    .1033801
     pts_s_n |   .1013231    .074363     1.36   0.178    -.0475863    .2502325
     corrupt |  -.1579303   .0489601    -3.23   0.002    -.2559712   -.0598893
      milm_j |  -.0005835   .0016394    -0.36   0.723    -.0038663    .0026994
us_un_friend |  -.0261931   .0134911    -1.94   0.057    -.0532086    .0008224
japan_un_f~d |   .0407745   .0305656     1.33   0.188    -.0204322    .1019811
uk_un_friend |   .0328692   .0323695     1.02   0.314    -.0319497    .0976881
france_un_~d |  -.0031448   .0282499    -0.11   0.912    -.0597143    .0534247
       indep |   .1052638   .0703668     1.50   0.140    -.0356432    .2461709
       _cons |   6.981901   4.983844     1.40   0.167    -2.998075    16.96188
-------------+----------------------------------------------------------------
     sigma_u |  4.8302321
     sigma_e |  .29411034
         rho |  .99630617   (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0:     F(26, 57) =    13.71              Prob > F = 0.0000

. est store fixed2

. hausman fixed2 random2

                 ---- Coefficients ----
             |      (b)          (B)            (b-B)     sqrt(diag(V_b-V_B))
             |     fixed2      random2       Difference          S.E.
-------------+----------------------------------------------------------------
        lpop |   -4.848811    -.6168131       -4.231998        2.216321
lgdp_pc_ppp |   -.2184703    -.2690165        .0505462        .2627927
     elrsacw |    .3184507     .1004888        .2179619        .0900371
    polity_n |    .0598919     .0293785        .0305134        .0120043
     pts_s_n |    .1013231     .0405079        .0608153        .0371059
     corrupt |   -.1579303    -.1543259       -.0036043        .0248082
      milm_j |   -.0005835    -.0010754         .000492        .0006674
us_un_friend |   -.0261931    -.0329801         .006787        .0044057
japan_un_f~d |    .0407745     .0455285        -.004754        .0205583
uk_un_friend |    .0328692     .0178181        .0150511        .0142267
france_un_~d |   -.0031448     .0135801       -.0167249        .0186247
       indep |    .1052638    -.0052071        .1104709        .0697258
------------------------------------------------------------------------------
                           b = consistent under Ho and Ha; obtained from xtreg
            B = inconsistent under Ha, efficient under Ho; obtained from xtreg

    Test:  Ho:  difference in coefficients not systematic

                 chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B)
                          =        9.04
                Prob>chi2 =      0.6999

On 04/04/06, William Gould, Stata <wgould@stata.com> wrote:
> Joana Quina <joana.statalist@gmail.com> reports,
>
>    1.  She has estimated the parameters of a model using -xtreg, fe- and
>        that the reported correlation between u_i and X_ij*b is .9249.
>
>    2.  She has estimated the parameters of the same same model on
>        the same data.  She then performs a Hausman test that fails to
>        reject random effects.
>
> She writes,
>
> > It seems counter-intuitive. Any suggestions would be much appreciated.
>
> It certainly does seem counterintuitive.  My first reaction is to suggest
> Joana check her work.
>
> Let's first understand just how counterintuitive this is.  The correlation
> between u_i and X_ij*b is .9249.  Now let's use an estimation method that
> constrains that correlation to be 0.  X_ij is fixed, so the only thing that
> can give is b.  The estimated b has got to change.  The Hausman tests basis
> its calculation on the change in b, and it reports that the change is small,
> relative to variance.
>
> That could could mean is that the variance is large, so large as to suggest
> that the model, estimated either way, is not worth much.  But Joana showed us
> (1) and the within R^2 was .6385, so let's dimiss that.
>
> However, X_ij is *NOT* necessarily fixed.  Joana could have included extra
> variables in the random-effects estimation, variables whose coefficients could
> not be estimated by the fixed-effects estimation.  In that case, the result is
> not counterintuitive at all.  Omit those variables, as done in the
> fixed-effects estimation, and u_i is correlated.  Include them, and the
> correlation vanishes.  Said differently, the subset of the b's estimated by
> both estimators did not change, and the extra b's estimated by the
> random-effects estimator eliminated the correlation.  This is exactly what one
> hopes will happen if one has a well-specified model.
>
> Is that what happened?
>
> -- Bill
> wgould@stata.com
> *
> *   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