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st: errors with reoprob


From   Caroline Shirk <[email protected]>
To   [email protected]
Subject   st: errors with reoprob
Date   Fri, 30 Jan 2009 12:17:53 -0500

Hi,

I am trying to use reoprob on several subsets of data, but am frequently returning an error that says "no observations," when there are in fact a few hundred or thousand observations.

Here is a sample of what has worked and has not:


. reoprob healthstatus_ mothed_1997 female age_1997 black hispanic otherrace imm bir > thwt lnincome_ lcstate_1997 after97 lcstate_after97 if insuredcurrent_1997==0, i(
> id)

Fitting constant-only model:

Iteration 0: log likelihood = -210.53583 Iteration 1: log likelihood = -203.35242 Iteration 2: log likelihood = -202.89348 Iteration 3: log likelihood = -202.88512 Iteration 4: log likelihood = -202.88512
Fitting full model:

Iteration 0: log likelihood = -193.35161 Iteration 1: log likelihood = -185.89095 Iteration 2: log likelihood = -185.42578 Iteration 3: log likelihood = -185.41409 Iteration 4: log likelihood = -185.41409 Random Effects Ordered Probit Number of obs = 166 LR chi2(12) = 34.94 Log likelihood = -185.41409 Prob > chi2 = 0.0005

------------------------------------------------------------------------------
healthstat~_ | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
eq1          |
mothed_1997 | -.1343246 .078087 -1.72 0.085 -.2873724 .0187232 female | -.1134465 .3173086 -0.36 0.721 -.7353599 .508467 age_1997 | .0762218 .0500739 1.52 0.128 -.0219213 .1743649 black | .9444976 .4461865 2.12 0.034 .0699881 1.819007 hispanic | -1.915888 1.679852 -1.14 0.254 -5.208337 1.37656 otherrace | -11.4049 8806.956 -0.00 0.999 -17272.72 17249.91 imm | 1.552197 1.595893 0.97 0.331 -1.575695 4.68009 birthwt | .0041629 .0327088 0.13 0.899 -.0599452 .068271 lnincome_ | .0494158 .1784086 0.28 0.782 -.3002586 .3990902 lcstate_1997 | -.1160115 .3668058 -0.32 0.752 -.8349378 .6029147 after97 | .0468742 .3186625 0.15 0.883 -.5776929 .6714413 lcstate_a~97 | -1.064486 .4077225 -2.61 0.009 -1.863607 -.2653644
-------------+----------------------------------------------------------------
_cut1        |
_cons | -.5617763 1.932602 -0.29 0.771 -4.349607 3.226054
-------------+----------------------------------------------------------------
_cut2        |
_cons | .7659718 1.940549 0.39 0.693 -3.037434 4.569377
-------------+----------------------------------------------------------------
_cut3        |
_cons | 2.267353 1.967665 1.15 0.249 -1.589199 6.123905
-------------+----------------------------------------------------------------
_cut4        |
_cons | 3.164919 2.00701 1.58 0.115 -.7687487 7.098586
-------------+----------------------------------------------------------------
rho          |
_cons | .503459 .1131211 4.45 0.000 .2817458 .7251722
------------------------------------------------------------------------------



reoprob healthstatus_ mothed_1997 female age_1997 black hispanic otherrace imm bir > thwt lnincome_ lcstate_1997 after97 lcstate_after97 if insuredcurrent_1997==1, i(
> id)
no observations
r(2000);



The second statement is exactly the same as the first, except for the "if" statement. The sub-sample in the first (working) regression has 302 ids, while the sub-sample in the second (non-working) regression has 2500. Any idea why this is happening?

Thanks,
Caroline
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