Thanks for your response. I tried the hausman test, but gives an error message (please see below). Also, I would like to know more about the incidental parameters problem, it would be great if you could point me towards a reference.
Thanks again,
Ali
. xi:xtlogit xest2 i.year_val lmis_per small diff3 i.client_cat i.product,i(code) fe
i.year_val _Iyear_val_1995-2004(naturally coded; _Iyear_val_1995 omitted)
i.client_cat _Iclient_ca_1-4 (naturally coded; _Iclient_ca_1 omitted)
i.product _Iproduct_1-7 (naturally coded; _Iproduct_1 omitted)
note: multiple positive outcomes within groups encountered.
note: 2 groups (12 obs) dropped due to all positive or
all negative outcomes.
Iteration 0: log likelihood = -95.770645
Iteration 1: log likelihood = -94.766928
Iteration 2: log likelihood = -94.765621
Iteration 3: log likelihood = -94.765621
Conditional fixed-effects logistic regression Number of obs = 194
Group variable (i): code Number of groups = 12
Obs per group: min = 6
avg = 16.2
max = 41
LR chi2(12) = 21.54
Log likelihood = -94.765621 Prob > chi2 = 0.0431
------------------------------------------------------------------------------
xest2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iyear_~1999 | .7705376 .5887671 1.31 0.191 -.3834246 1.9245
_Iyear_~2000 | .0804127 .5377096 0.15 0.881 -.9734788 1.134304
lmis_per | .1431241 .1035589 1.38 0.167 -.0598477 .3460958
small | -.6050289 .4629784 -1.31 0.191 -1.51245 .3023921
diff3 | .622433 .3797323 1.64 0.101 -.1218286 1.366695
_Iclient_c~2 | -2.113278 1.297228 -1.63 0.103 -4.655798 .4292424
_Iclient_c~3 | -.2535826 .649904 -0.39 0.696 -1.527371 1.020206
_Iclient_c~4 | .0658344 .7872134 0.08 0.933 -1.477076 1.608744
_Iproduct_3 | .6620156 .4768083 1.39 0.165 -.2725115 1.596543
_Iproduct_5 | -.2281419 .5510209 -0.41 0.679 -1.308123 .8518393
_Iproduct_6 | .7829757 .6403594 1.22 0.221 -.4721057 2.038057
_Iproduct_7 | .0597629 .9041543 0.07 0.947 -1.712347 1.831873
------------------------------------------------------------------------------
. est store fixed
. xi:logit xest2 i.year_val lmis_per small diff3 i.client_cat i.product if e(sample)
i.year_val _Iyear_val_1995-2004(naturally coded; _Iyear_val_1995 omitted)
i.client_cat _Iclient_ca_1-4 (naturally coded; _Iclient_ca_1 omitted)
i.product _Iproduct_1-7 (naturally coded; _Iproduct_1 omitted)
Iteration 0: log likelihood = -134.42931
Iteration 1: log likelihood = -121.44682
Iteration 2: log likelihood = -121.24036
Iteration 3: log likelihood = -121.23977
Iteration 4: log likelihood = -121.23977
Logistic regression Number of obs = 194
LR chi2(12) = 26.38
Prob > chi2 = 0.0095
Log likelihood = -121.23977 Pseudo R2 = 0.0981
------------------------------------------------------------------------------
xest2 | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iyear_~1999 | .7388409 .5108352 1.45 0.148 -.2623778 1.740059
_Iyear_~2000 | -.0084935 .4215546 -0.02 0.984 -.8347253 .8177384
lmis_per | .1913278 .0814752 2.35 0.019 .0316393 .3510163
small | -.3147586 .4116586 -0.76 0.445 -1.121595 .4920774
diff3 | .3532745 .3432509 1.03 0.303 -.3194849 1.026034
_Iclient_c~2 | -2.458576 1.211284 -2.03 0.042 -4.83265 -.0845026
_Iclient_c~3 | -.0676932 .4828449 -0.14 0.889 -1.014052 .8786653
_Iclient_c~4 | -.0977617 .5403883 -0.18 0.856 -1.156903 .96138
_Iproduct_3 | .4145465 .4540468 0.91 0.361 -.4753688 1.304462
_Iproduct_5 | -.3205893 .5280656 -0.61 0.544 -1.355579 .7144003
_Iproduct_6 | .5438465 .6083991 0.89 0.371 -.6485937 1.736287
_Iproduct_7 | -.1268551 .8296969 -0.15 0.878 -1.753031 1.499321
_cons | -1.761016 .5969357 -2.95 0.003 -2.930988 -.5910432
------------------------------------------------------------------------------
. hausman fixed
no coefficients in common; specify equations(matchlist)
for problems with different equation names.
r(498);
>>> [email protected] 11/17 9:11 PM >>>
a hausman test (see hausman) can be used since OLS is efficient if the fixed effects are zero but biased if they are not and FE logit is consistent in either case. However, be aware that the FE logit model is only run on observations that have a change in the outcome variable over the panel dimension. You will want to the run the simple logit on just this sub-sample for the hausman test.
the approach you propose will not give you a correct results because unless you have more observations in the panel dimension (ie year) than number of panels (ie individuals) just adding dummy variables will result in biased estimates because of the incidental parameters problem.
-----Original Message-----
From: [email protected]
[mailto:[email protected]]On Behalf Of Ali Karim
Sent: Friday, November 18, 2005 12:44 PM
To: [email protected]
Subject: st: fixed effect logit vs naive logit
Dear subscribers:
I was wondering if there is any way I can test whether the fixed-effects logit model is a better fit than the na�ve logit model.
for example:
xtlogit outcome age sex race,i(id) fe
vs.
logit outcome age sex race
The only way I figured to do this is to run a logit model with dummies for id, then run the test for all the dummies jointly equals zero.
Thanks in advance.
Ali
Ali Mehryar Karim
Senior Quantitative Analyst
DELIVER/John Snow, Inc. (JSI)
1616 N Fort Myer Dr, 11th Floor
Arlington, VA 22209
ph: 703-528-7474; fx: 703-528-7480
visit the DELIVER website: deliver.jsi.com
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