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From |
"Roy, Manan" <mroy@mail.smu.edu> |

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

Subject |
st: RE: triprobit convergence problem |

Date |
Mon, 22 Mar 2010 10:54:56 -0500 |

Hi Stephen, 1) I used -triprobit- on ssc. 2) The data is on adults 25-59 years old with at least 1 child between 5-18 years old (N=1600), and adults between 25-59 with at least 1 child between 5-10 years old (N=1027). The model is trying to identify the effect of school meal program participation on different time use categories. 3) TIME4_0, NSLP, SMEAL, male, WNonHisp South are all dummy variables 4) This exact same trivariate model with TIME4_0, however, converges with the data set with N=1027, without any options. 5) The following output is for the data set with N=1600 (a) triprobit (TIME4_0 = NSLP SMEAL male teage agesq WNonHisp South) (NSLP = male teage agesq WNonHisp South) (SMEAL = male teage agesq WNonHisp South) [w=eufinlwgt], difficult (analytic weights assumed) trivariate probit, GHK simulator, 25 draws Comparison log likelihood = -2995.3698 initial: log likelihood = -2995.3698 rescale: log likelihood = -2995.3698 rescale eq: log likelihood = -2995.3698 Iteration 0: log likelihood = -2995.3698 Iteration 1: log likelihood = -2810.0233 (not concave) Iteration 2: log likelihood = -2798.2733 (not concave) Iteration 3: log likelihood = -2790.7672 (not concave) Iteration 4: log likelihood = -2790.6208 (not concave) Iteration 5: log likelihood = -2790.2056 (not concave) Iteration 6: log likelihood = -2789.4005 (not concave) Iteration 7: log likelihood = -2789.3472 (not concave) Iteration 8: log likelihood = -2789.1896 (not concave) Iteration 9: log likelihood = -2788.9758 (not concave) Iteration 10: log likelihood = -2788.3554 (not concave) Iteration 11: log likelihood = -2788.204 (not concave) Iteration 12: log likelihood = -2787.9404 (not concave) Iteration 13: log likelihood = -2787.9003 (not concave) Iteration 14: log likelihood = -2787.6958 (not concave) Iteration 15: log likelihood = -2787.3079 (not concave) Iteration 16: log likelihood = -2787.1486 (not concave) Iteration 17: log likelihood = -2786.8424 (not concave) Iteration 18: log likelihood = -2786.8261 (not concave) Iteration 19: log likelihood = -2786.7436 (not concave) Iteration 20: log likelihood = -2786.6794 (not concave) numerical derivatives are approximate nearby values are missing numerical derivatives are approximate nearby values are missing numerical derivatives are approximate nearby values are missing Iteration 21: log likelihood = -2786.6747 (not concave) Iteration 22: log likelihood = -2786.6708 (not concave) could not calculate numerical derivatives missing values encountered r(430); (b) triprobit (TIME4_0 = NSLP SMEAL male teage agesq WNonHisp South) (NSLP = male teage agesq WNonHisp South) (SMEAL = male teage agesq WNonHisp South) [w=eufinlwgt], difficult draws(45) (analytic weights assumed) trivariate probit, GHK simulator, 45 draws Comparison log likelihood = -2995.3698 initial: log likelihood = -2995.3698 rescale: log likelihood = -2995.3698 rescale eq: log likelihood = -2995.3698 Iteration 0: log likelihood = -2995.3698 Iteration 1: log likelihood = -2876.4216 Iteration 2: log likelihood = -2801.6882 Iteration 3: log likelihood = -2795.6717 Iteration 4: log likelihood = -2794.0842 (backed up) Iteration 5: log likelihood = -2791.9006 Iteration 6: log likelihood = -2790.852 (not concave) Iteration 7: log likelihood = -2790.7997 (not concave) Iteration 8: log likelihood = -2790.7166 (not concave) Iteration 9: log likelihood = -2790.6944 (not concave) Iteration 10: log likelihood = -2790.5223 (not concave) Iteration 11: log likelihood = -2790.4437 (not concave) Iteration 12: log likelihood = -2790.4041 (not concave) Iteration 13: log likelihood = -2790.375 (not concave) Iteration 14: log likelihood = -2790.3516 (not concave) Iteration 15: log likelihood = -2790.3329 (not concave) Iteration 16: log likelihood = -2790.3146 (not concave) Iteration 17: log likelihood = -2790.0194 (not concave) Iteration 18: log likelihood = -2789.7409 (not concave) Iteration 19: log likelihood = -2789.5673 (not concave) Iteration 20: log likelihood = -2789.1887 (not concave) Iteration 21: log likelihood = -2789.1792 (not concave) Iteration 22: log likelihood = -2789.0136 (not concave) Iteration 23: log likelihood = -2788.9393 (not concave) Iteration 24: log likelihood = -2788.8466 (not concave) Iteration 25: log likelihood = -2788.8227 (not concave) Iteration 26: log likelihood = -2788.7829 (not concave) Iteration 27: log likelihood = -2788.7466 (not concave) Iteration 28: log likelihood = -2788.6929 (not concave) Iteration 29: log likelihood = -2788.5878 (not concave) Iteration 30: log likelihood = -2788.5645 (not concave) Iteration 31: log likelihood = -2788.5494 (not concave) Iteration 32: log likelihood = -2788.5383 (not concave) Iteration 33: log likelihood = -2788.5355 (not concave) Iteration 34: log likelihood = -2788.5213 (not concave) numerical derivatives are approximate nearby values are missing numerical derivatives are approximate nearby values are missing numerical derivatives are approximate nearby values are missing Iteration 35: log likelihood = -2788.5166 (not concave) Iteration 36: log likelihood = -2788.5163 (not concave) could not calculate numerical derivatives missing values encountered r(430); 6) Will try the other options. Thanks! Manan ________________________________________ From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] On Behalf Of Stephen P. Jenkins [stephenj@essex.ac.uk] Sent: Monday, March 22, 2010 7:59 AM To: statalist@hsphsun2.harvard.edu Subject: st: triprobit convergence problem ------------------------------ Date: Sun, 21 Mar 2010 17:27:20 -0500 From: "Roy, Manan" <mroy@mail.smu.edu> Subject: st: triprobit convergence problem Hi, I am trying to estimate triprobit models with different time categories (as dummies) and 2 binary program participation variables. I have 2 almost identical data sets, one with N=1600 and the other with N=1000. There are 2 time categories for which the models are not converging. Let's call them TIME1 and TIME2.=20 TIME1 converges in N=1600 data while it doesn't in N=1000 data. The exactly opposite case holds for TIME2. I have tried using the technique option. However, I get the error that this option's not allowed with triprobit. I have also tried the difficult option, and specified different number of draws. Any suggestions on how I can make it work will be greatly appreciated. Thanks, Manan >>>>>>>>>>>>>>>>>>> You should state the source of the user-written program -triprobit- (it is on SSC, I believe) You do not provide, as the Statalist FAQ asks, the precise Stata commands that you typed and the output that was produced. And, sorry, the nature of your trivariate probit specification is unclear from what you write, in any case. There are at least 3 other ways to estimate trivariate probit models, and you could try them (they also allow -maximize- options like -difficult- and -technique(...)-: * -mvprobit- on SSC * Generic code using a plugin (and so fast): see Cappellari & Jenkins, Stata Journal 6(2), 2006 [article downloadable from Stata Journal website] * -cmp- on SSC Stephen ------------------------------------- Professor Stephen P. Jenkins <stephenj@essex.ac.uk> Institute for Social and Economic Research (ISER) University of Essex, Colchester CO4 3SQ, UK Tel: +44(0)1206 873374. Fax: +44(0)1206 873151 http://www.iser.essex.ac.uk Survival Analysis using Stata: http://www.iser.essex.ac.uk/survival-analysis Downloadable papers and software: http://ideas.repec.org/e/pje7.html * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/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/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: RE: triprobit convergence problem***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**References**:**st: triprobit convergence problem***From:*"Stephen P. Jenkins" <stephenj@essex.ac.uk>

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