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| From | Leandro Brufman <leandrobrufman@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | st: intreg cluster vs tobit cluster (one reports some missing std errors, the other doesn't) |
| Date | Thu, 4 Nov 2010 20:18:02 -0400 |
Hi everyone!
I was running a Tobit model with clustered errors using -intreg
varlist, clustered(clustervar)- as detailed in here
(http://www.stata.com/support/faqs/stat/tobit.html). Sometimes the
standard errors of a couple of coefficients appeared as missing. After
reading a lot of useful things here in Statalist I couldn't find the
problem behind those results (for example, I never had 1 observation
per cluster, which was pointed out as a possible problem by Mark
Schaefer, I fixed a scale problem in one variable that was driving a
couple of missing std errors, but not all of them, etc.).
Just by chance I found one article of Woolridge (2006)
(https://www.msu.edu/~ec/faculty/wooldridge/current%20research/clus1aea.pdf)
At the end it says that you can run tobit with clustered errors by
typing -tobit varlist, ll(0) cluster(clustervar)-
I was just curious about that (I thought that tobit didn't allow
cluster option). I run it and guess what.... the missing std errors
dissapeared!
Below you'll see the results. Any ideas of why is this happening????
*********** BEGIN EXAMPLE **************************
. intreg amt_mt2 amt_mt3 ip_vsam_ipolate_w cridum ccc*, cluster(cricode)
Fitting constant-only model:
Iteration 0: log pseudolikelihood = -8848.0525
Iteration 1: log pseudolikelihood = -1547.301
Iteration 2: log pseudolikelihood = -1425.1908
Iteration 3: log pseudolikelihood = -1407.5083
Iteration 4: log pseudolikelihood = -1407.1085
Iteration 5: log pseudolikelihood = -1407.1083
Iteration 6: log pseudolikelihood = -1407.1083
Fitting full model:
Iteration 0: log pseudolikelihood = -8834.4116
Iteration 1: log pseudolikelihood = -1526.3341
Iteration 2: log pseudolikelihood = -1366.9358
Iteration 3: log pseudolikelihood = -1332.2446
Iteration 4: log pseudolikelihood = -1331.6778
Iteration 5: log pseudolikelihood = -1331.6745
Iteration 6: log pseudolikelihood = -1331.6745
Interval regression Number of obs = 6757
Wald chi2(11) = 996.00
Log pseudolikelihood = -1331.6745 Prob > chi2 = 0.0000
(Std. Err. adjusted for 21 clusters in cricode)
------------------------------------------------------------------------------
| Robust
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ip_vsam_ip~w | -.0263045 . . . . .
cridum | -156.2131 50.9088 -3.07 0.002 -255.9925 -56.43371
ccc1 | 219.9064 70.44264 3.12 0.002 81.84139 357.9715
ccc2 | 442.6939 147.2167 3.01 0.003 154.1545 731.2333
ccc3 | 97.37137 32.76476 2.97 0.003 33.15362 161.5891
ccc4 | 555.3243 185.262 3.00 0.003 192.2175 918.4311
ccc5 | 421.7124 142.3647 2.96 0.003 142.6827 700.7422
ccc6 | 269.146 87.67189 3.07 0.002 97.31224 440.9797
ccc8 | 96.5494 37.97573 2.54 0.011 22.11834 170.9804
ccc9 | 36.67169 13.64486 2.69 0.007 9.928247 63.41513
ccc10 | 179.9544 60.99526 2.95 0.003 60.40586 299.5029
ccc11 | 58.70021 28.58481 2.05 0.040 2.67502 114.7254
_cons | -300.8123 94.71781 -3.18 0.001 -486.4558 -115.1688
-------------+----------------------------------------------------------------
/lnsigma | 4.989588 .3018269 16.53 0.000 4.398018 5.581157
-------------+----------------------------------------------------------------
sigma | 146.8758 44.33108 81.28957 265.3786
------------------------------------------------------------------------------
Observation summary: 6613 left-censored observations
144 uncensored observations
0 right-censored observations
0 interval observations
. tobit amt_mt3 ip_vsam_ipolate_w cridum ccc*, ll(0) cluster(cricode)
note: ccc7 dropped because of collinearity
Tobit regression Number of obs = 6757
F( 12, 6745) = 287.30
Prob > F = 0.0000
Log pseudolikelihood = -1331.6745 Pseudo R2 = 0.0536
(Std. Err. adjusted for 21 clusters in cricode)
------------------------------------------------------------------------------
| Robust
amt_mt3 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ip_vsam_ip~w | -.0263045 .0088692 -2.97 0.003 -.043691 -.008918
cridum | -156.2131 50.9064 -3.07 0.002 -256.0057 -56.42051
ccc1 | 219.9064 70.44073 3.12 0.002 81.82035 357.9925
ccc2 | 442.6939 147.2129 3.01 0.003 154.1102 731.2777
ccc3 | 97.37137 32.76389 2.97 0.003 33.1438 161.5989
ccc4 | 555.3243 185.2573 3.00 0.003 192.1616 918.487
ccc5 | 421.7124 142.3617 2.96 0.003 142.6385 700.7864
ccc6 | 269.146 87.66958 3.07 0.002 97.28593 441.006
ccc8 | 96.5494 37.97516 2.54 0.011 22.10609 170.9927
ccc9 | 36.67169 13.64448 2.69 0.007 9.924197 63.41918
ccc10 | 179.9544 60.99367 2.95 0.003 60.38752 299.5212
ccc11 | 58.70021 28.58445 2.05 0.040 2.665667 114.7348
_cons | -300.8123 94.71551 -3.18 0.002 -486.4846 -115.14
-------------+----------------------------------------------------------------
/sigma | 146.8758 44.33002 59.97501 233.7767
------------------------------------------------------------------------------
Obs. summary: 6613 left-censored observations at amt_mt3<=0
144 uncensored observations
0 right-censored observations
. compare amt_mt2 amt_mt3 // just to show you that the vars are ok,
amt_mt2 is missing whenever amt_mt3==0, and both are equal otherwise.
---------- difference ----------
count minimum average maximum
------------------------------------------------------------------------
amt_mt2=amt_mt3 152
----------
jointly defined 152 0 0 0
amt_mt2 missing only 6639
----------
total 6791
.
*************** END EXAMPLE ********************************
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