All,
Hiya! After a very helpful and constructive private dialogue with May
Boggess at StataCorp, I was told that, in fitting a -clogit- model, the
'individual'-specific variables (ISVs) (i.e., independent variables which
are interacted with the alternative-specfic constants) _must_ have their
parameters initially constrained. Quite a revelation, given that no
mention is made of this in quite a number of texts that set out the
conditional (mixed) logit model.
After eventually working out how to set about doing it, my final (edited)
output for the 'null' model was this:
. clogit winner edchange edchgsqd spending spendsqd letoutXcon letoutXlab
letoutXldm clmargXcon clmargXlab cdmargXcon cdmargXldm ldmargXlab
dmargXldm classXcon classXlab classXldm, group(id) constraints(1 2 3 4 5 6
7 8 9 10 11 12) or
Iteration 0: log likelihood = -2604.3979
Iteration 1: log likelihood = -1531.3854
Iteration 2: log likelihood = -1457.0306
Iteration 3: log likelihood = -1455.6393
Iteration 4: log likelihood = -1455.6368
Iteration 5: log likelihood = -1455.6368
Conditional (fixed-effects) logit regression Number of obs = 10985
Wald chi2(4) = 1535.80
Log likelihood = -1455.6368 Prob > chi2 = 0.0000
( 1) [winner]letoutXcon = 0
( 2) [winner]letoutXlab = 0
( 3) [winner]letoutXldm = 0
( 4) [winner]clmargXcon = 0
( 5) [winner]clmargXlab = 0
( 6) [winner]cdmargXcon = 0
( 7) [winner]cdmargXldm = 0
( 8) [winner]ldmargXlab = 0
( 9) [winner]ldmargXldm = 0
(10) [winner]classXcon = 0
(11) [winner]classXlab = 0
(12) [winner]classXldm = 0
----------------------------------------------------------------------------
winner | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-----------+----------------------------------------------------------------
edchange | .0001503 .0000605 -21.86 0.000 .0000683 .000331
edchgsqd | 1020589 2276743 6.20 0.000 12881.91 8.09e+07
spending | .2908725 .1378165 -2.61 0.009 .1149212 .7362161
spendsqd | 234.0945 117.4051 10.88 0.000 87.59698 625.5951
letoutXcon | 1 . . . . .
letoutXlab | 1 . . . . .
letoutXldm | 1 . . . . .
clmargXcon | 1 . . . . .
clmargXlab | 1 . . . . .
cdmargXcon | 1 . . . . .
cdmargXldm | 1 . . . . .
ldmargXlab | 1 . . . . .
ldmargXldm | 1 . . . . .
classXcon | 1 . . . . .
classXlab | 1 . . . . .
classXldm | 1 . . . . .
----------------------------------------------------------------------------
. est store nmodel
. lrtest fmodel nmodel
LR test likely invalid for models with robust vce
r(498);
. lrtest fmodel nmodel, force
likelihood-ratio test LR chi2(12) = 1813.60
(Assumption: nmodel nested in fmodel) Prob > chi2 = 0.0000
Although May didn't actually say the parameters were to be constrained to
zero, I assumed this was what she meant and it does appear sensible.
However, although I finally got what I wanted (a test result which appears
to strongly confirm that the ISVs included in the full model should be
retained), -lrtest- had to be -force-d to run, as you can see.
Is it safe to accept this result given the rather ominous error message
that appeared after -lrtest- was first run? I'm not sure, but it does
represent an emormous difference in the model log-likelihoods for only 12
df (the critical chi-squared value at p=.05 is 21.026). There is no other
way do such a test after -clogit- as far as I'm aware.
Thanks in anticipation.
CLIVE NICHOLAS |t: 0(044)7903 397793
Politics |e: [email protected]
Newcastle University |http://www.ncl.ac.uk/geps
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