Hi All,

I have a question about xtreg and xtmixed.

If I enter the following command I get this result.

xtset interven2

xtreg difftot i.gender2


Random-effects GLS regression Number of obs = 85
Group variable: interven2 Number of groups = 3

R-sq: within = 0.0015 Obs per group: min = 13
between = 0.1840 avg = 28.3
overall = 0.0007 max = 55

Wald chi2(2) = 0.06
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.9722

----------------------------------------------------------------------------------
difftot | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
gender2 |
Female | .0294211 .151446 0.19 0.846 -.2674076 .3262497
Non-bin | -.0499999 .4162483 -0.12 0.904 -.8658316 .7658317
|
_cons | .281 .0724595 3.88 0.000 .1389819 .4230181
-----------------+----------------------------------------------------------------
sigma_u | 0
sigma_e | .55706407
rho | 0 (fraction of variance due to u_i)
----------------------------------------------------------------------------------

However if I request mle with xtreg I get the following result.

xtreg difftot i.gender2, mle

Fitting constant-only model:
Iteration 0: log likelihood = -72.359585
Iteration 1: log likelihood = -72.330535
Iteration 2: log likelihood = -72.33045

Fitting full model:
Iteration 0: log likelihood = -72.427795
Iteration 1: log likelihood = -72.067824
Iteration 2: log likelihood = -72.017598
Iteration 3: log likelihood = -72.013335
Iteration 4: log likelihood = -72.013311

Random-effects ML regression Number of obs = 85
Group variable: interven2 Number of groups = 3

Random effects u_i ~ Gaussian Obs per group: min = 13
avg = 28.3
max = 55

LR chi2(2) = 0.63
Log likelihood = -72.013311 Prob > chi2 = 0.7282

----------------------------------------------------------------------------------
difftot | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
gender2 |
Female | -.0463684 .1571585 -0.30 0.768 -.3543934 .2616566
Non-bin | -.3585661 .4490142 -0.80 0.425 -1.238618 .5214855
|
_cons | .3761473 .1436128 2.62 0.009 .0946713 .6576232
-----------------+----------------------------------------------------------------
/sigma_u | .1827952 .1268277 .0469224 .7121132
/sigma_e | .5517788 .0434 .4729487 .6437482
rho | .0988951 .1260109 .0033984 .5520696
----------------------------------------------------------------------------------
Likelihood-ratio test of sigma_u=0: chibar2(01)= 1.44 Prob>=chibar2 = 0.115


Why should the coefficients and sigma_u variance change?
Similarly this also occurs with xtmixed as can be seen with the following results just below.

It has to do with the constant which I notice is mentioned in the first line of the just previous output but I don't understand how.

Also, the results for xtmixed seem to be opposite to the results from xtreg in the sense that where I request no constant with xtmixed, the results are
identical to where xtreg does not have a constant only model - the first model above..

An explanation of why this is happening would be much appreciated.

xtmixed difftot i.gender2 || interven2:, mle

Performing EM optimization:

Performing gradient-based optimization:

Iteration 0: log likelihood = -72.013312
Iteration 1: log likelihood = -72.013311

Computing standard errors:

Mixed-effects ML regression Number of obs = 85
Group variable: interven2 Number of groups = 3

Obs per group: min = 13
avg = 28.3
max = 55


Wald chi2(2) = 0.77
Log likelihood = -72.013311 Prob > chi2 = 0.6805

----------------------------------------------------------------------------------
difftot | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
gender2 |
Female | -.0463684 .1537466 -0.30 0.763 -.3477063 .2549695
Non-bin|Diff-ID | -.3585661 .4175263 -0.86 0.390 -1.176903 .4597703
|
_cons | .3761473 .1364022 2.76 0.006 .1088039 .6434906
----------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
interven2: Identity |
sd(_cons) | .1827952 .1268308 .0469209 .7121365
-----------------------------+------------------------------------------------
sd(Residual) | .5517788 .0434 .4729487 .6437482
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) = 1.44 Prob >= chibar2 = 0.1151


xtmixed difftot i.gender2 || interven2:, mle noconstant

Note: all random-effects equations are empty; model is linear regression

Mixed-effects ML regression Number of obs = 85

Wald chi2(2) = 0.06
Log likelihood = -72.733388 Prob > chi2 = 0.9712

----------------------------------------------------------------------------------
difftot | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
gender2 |
Female | .0294211 .1487494 0.20 0.843 -.2621224 .3209645
Non-bin|Diff-ID | -.0499999 .4088367 -0.12 0.903 -.8513052 .7513053
|
_cons | .281 .0711693 3.95 0.000 .1415107 .4204894
----------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
sd(Residual) | .5693547 .0436675 .4898902 .6617091
------------------------------------------------------------------------------

Thanks in advance,

Don
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