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st: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations
From
nick klein <[email protected]>
To
[email protected]
Subject
st: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations
Date
Wed, 5 Jun 2013 19:09:40 -0400
Hi-
I have a question about -xtnbreg-
I’m curious if anyone has any thoughts on I am getting the same
results are when I limit the number of iterations to 100 and when I
let the model run until it converges (sometimes after 10,000
iterations).
I have a large panel dataset and am running a series of models. When I
let the models run overnight, the models converge after somewhere
between 50 and 15,000 iterations depending on the model. Looking at
the log, I can see that the models says "(not concave)" through almost
all the iterations but do eventually converge. And the model
coefficients seem to make sense.
However, yesterday I was testing out some my code and ran a model
where I limited the number of iterations to 100 (using the iterate(#)
option). The results for the model were exactly the same in both cases
(100 or >10,000 iterations). I can tell that in the steps right before
fitting the final model, the log likelihood for each iteration become
very similar (I assume if I did a trace, I'd see that they are exactly
the same) - i'm just not sure why this might be or what it means for
my analysis.
Anyone have thoughts on why this might be?
Below are a few of the relevant commands and then part of each log
file - showing the beginning and final iterations for the xtnbreg
model (first without limiting the number of iterations).
-Thanks
Nick
--------------
. xtset
panel variable: FIPSblock (strongly balanced)
time variable: Year, 1991 to 2008
delta: 1 unit
. xtsum Firm_Births
Variable | Mean Std. Dev. Min Max | Observations
-----------------+--------------------------------------------+----------------
Firm_B~s overall | .3413659 2.135944 0 141 | N = 504072
between | 1.808005 0 69.05556 | n = 28004
within | 1.137315 -59.71419 94.73025 | T = 18
. xtnbreg Firm_Births $base_vars $spatial_vars $demographic_vars
$year_vars, re exposure(Acres)
Fitting negative binomial (constant dispersion) model:
Iteration 0: log likelihood = -51795942 (not concave)
Iteration 1: log likelihood = -49724759 (not concave)
Iteration 2: log likelihood = -48730264 (not concave)
Iteration 3: log likelihood = -47170895 (not concave)
Iteration 4: log likelihood = -45963320 (not concave)
Iteration 5: log likelihood = -45021992 (not concave)
....
Iteration 9255:log likelihood = -470774.97 (not concave)
Iteration 9256:log likelihood = -470671.29 (not concave)
Iteration 9257:log likelihood = -470567.37
Iteration 9258:log likelihood = -402031.39 (backed up)
Iteration 9259:log likelihood = -388353.2
Iteration 9260:log likelihood = -387803.5
Iteration 9261:log likelihood = -387800.57
Iteration 9262:log likelihood = -387800.57
Iteration 0: log likelihood = -475132.13
Iteration 1: log likelihood = -458154.04
Iteration 2: log likelihood = -457787.12
Iteration 3: log likelihood = -457787.1
Iteration 0: log likelihood = -337007.31
Iteration 1: log likelihood = -305214.12
Iteration 2: log likelihood = -303965.91
Iteration 3: log likelihood = -303963.67
Iteration 4: log likelihood = -303963.67
Fitting full model:
Iteration 0: log likelihood = -261858.51
Iteration 1: log likelihood = -240203.89
Iteration 2: log likelihood = -238845.87
Iteration 3: log likelihood = -238800.18
Iteration 4: log likelihood = -238800.08
Iteration 5: log likelihood = -238800.08
Random-effects negative binomial regression Number of obs = 504072
Group variable: FIPSblock Number of groups = 28004
Random effects u_i ~ Beta Obs per group: min = 18
avg = 18.0
max = 18
Wald chi2(34) = 57551.00
Log likelihood = -238800.08 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------------------
Firm_Births | Coef. Std. Err. z P>|z|
[95% Conf. Interval]
----------------------------+----------------------------------------------------------------
...
_cons | -2.502535 .0503268 -49.73 0.000
-2.601174 -2.403897
ln(Acres) | 1 (exposure)
----------------------------+----------------------------------------------------------------
/ln_r | 2.151578 .0221937
2.108079 2.195077
/ln_s | -.3551327 .0117288
-.3781207 -.3321448
----------------------------+----------------------------------------------------------------
r | 8.598419 .1908308
8.232415 8.980694
s | .7010804 .0082228
.6851478 .7173835
---------------------------------------------------------------------------------------------
Likelihood-ratio test vs. pooled: chibar2(01) = 1.3e+05 Prob>=chibar2 = 0.000
(est1 stored)
. xtnbreg Firm_Births $base_vars $spatial_vars $demographic_vars
$year_vars, re exposure(Acres) iterate(100)
Fitting negative binomial (constant dispersion) model:
Iteration 0: log likelihood = -51795942 (not concave)
Iteration 1: log likelihood = -49724759 (not concave)
Iteration 2: log likelihood = -48730264 (not concave)
Iteration 3: log likelihood = -47170895 (not concave)
Iteration 4: log likelihood = -45963320 (not concave)
Iteration 5: log likelihood = -45021992 (not concave)
....
Iteration 97: log likelihood = -6911325.4 (not concave)
Iteration 98: log likelihood = -6837625.2 (not concave)
Iteration 99: log likelihood = -6480527.6 (not concave)
Iteration 100: log likelihood = -6375502.1 (not concave)
convergence not achieved
Iteration 0: log likelihood = -475132.13
Iteration 1: log likelihood = -458154.04
Iteration 2: log likelihood = -457787.12
Iteration 3: log likelihood = -457787.1
Iteration 0: log likelihood = -457787.1 (not concave)
Iteration 1: log likelihood = -447149.68 (not concave)
Iteration 2: log likelihood = -433018.99
Iteration 3: log likelihood = -374195.22
Iteration 4: log likelihood = -343626.28
Iteration 5: log likelihood = -325391.13
Iteration 6: log likelihood = -308563.02
Iteration 7: log likelihood = -304292.41
Iteration 8: log likelihood = -303973.76
Iteration 9: log likelihood = -303963.67
Iteration 10: log likelihood = -303963.67
Fitting full model:
Iteration 0: log likelihood = -261858.51
Iteration 1: log likelihood = -240203.89
Iteration 2: log likelihood = -238845.87
Iteration 3: log likelihood = -238800.18
Iteration 4: log likelihood = -238800.08
Iteration 5: log likelihood = -238800.08
Random-effects negative binomial regression Number of obs = 504072
Group variable: FIPSblock Number of groups = 28004
Random effects u_i ~ Beta Obs per group: min = 18
avg = 18.0
max = 18
Wald chi2(34) = 57551.00
Log likelihood = -238800.08 Prob > chi2 = 0.0000
---------------------------------------------------------------------------------------------
Firm_Births | Coef. Std. Err. z P>|z|
[95% Conf. Interval]
----------------------------+----------------------------------------------------------------
...
_cons | -2.502535 .0503268 -49.73 0.000
-2.601174 -2.403897
ln(Acres) | 1 (exposure)
----------------------------+----------------------------------------------------------------
/ln_r | 2.151578 .0221937
2.108079 2.195077
/ln_s | -.3551327 .0117288
-.3781207 -.3321448
----------------------------+----------------------------------------------------------------
r | 8.598419 .1908308
8.232415 8.980694
s | .7010804 .0082228
.6851478 .7173835
---------------------------------------------------------------------------------------------
Likelihood-ratio test vs. pooled: chibar2(01) = 1.3e+05 Prob>=chibar2 = 0.000
(est1 stored)
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