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From |
nick klein <nick.auxiliary@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

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) * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations***From:*"William Buchanan" <william@williambuchanan.net>

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