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From |
"William Buchanan" <william@williambuchanan.net> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations |

Date |
Tue, 25 Jun 2013 10:48:16 -0700 |

Check out -help maximize-. It's possible that the model didn't truly converge, but just reached the default maximum number of iterations. In either case, if you see the not concave message repeatedly like that it is generally a good idea to look at simplifying your model and trying to identifying which variable is causing the model to fail to converge in a reasonable amount of time. HTH, Billy -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of nick klein Sent: Wednesday, June 05, 2013 4:10 PM To: statalist@hsphsun2.harvard.edu Subject: st: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations 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/ * * 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/

**References**:**st: xtnbreg - same results after convergence at 9,000 iterations or limiting to 100 iterations***From:*nick klein <nick.auxiliary@gmail.com>

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