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From |
Muhammad Anees <anees@aneconomist.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RE: synthetic ZINB |

Date |
Mon, 6 Jun 2011 11:45:25 +0500 |

Thanks Dr. Joseph Hilbe! The book provides extremely useful content for us interested in count data analysis. I appreciate your work on Negative Binomial Regression in detail. Anees On Mon, Jun 6, 2011 at 11:13 AM, Ari Samaranayaka <ari.samaranayaka@ipru.otago.ac.nz> wrote: > Hi Joseph > Thank you for providing codes and directing us towards a useful book. > Ari > > On 6/06/2011 11:55 a.m., jhilbe@aol.com wrote: >> >> oops. In the zinb_syn.do code I neglected to amend the label just prior to >> the synthetic zinb model at the end. The hurdle model caption was retained. >> I am attaching the correct zinb._syn.do program, and a similar program for >> synthetic ZIP. The code >> runs OK, it was simply the caption. My apologies. >> >> Joseph Hilbe >> >> >> >> -----Original Message----- >> From: jhilbe <jhilbe@aol.com> >> To: statalist <statalist@hsphsun2.harvard.edu> >> Sent: Sun, Jun 5, 2011 4:31 pm >> Subject: RE: synthetic ZINB >> >> Statalisters: >> >> I happened to see a discussion of synthetic ZINB data on the StataList >> digest today. There is an entirely different way to approach this - one >> that creates a full synthetic ZINB model. I wrote about creating >> synthetic models in the first volume of the 2010 Stata Journal, and >> discuss them much more fully in my recently published, second edition >> of "Negative Binomial Regression" (Cambridge University Press, 572 >> pages). The book discusses most every count model in the literature, >> providing both Stata and R code for examples. Output is given in Stata, >> except for the final chapter on Bayesian NB models. I also develop a >> variety of synthetic count models where it is simple to write your >> chosen synthetic predictors as continuous predictors, as binary, or as >> multilevel categorical. You may employ as many predictors as you wish, >> from an intercept-only model to one with more than 10 predictors if you >> wish. The user specifes the desired coefficients for all predictors, as >> well as levels of predictor. For NB models you also declare the value >> of alpha you wish to model. >> >> It was quite simple to convert the synthetic NB2-logit hurdle model I >> give in the book to a zero-inflated NB model, with a logit binary >> component. I am attaching it to this message, but provide it below my >> signature as well, together with a sample run. Note where the >> coefficient values are defined in the comment above active code, but >> the actual values are given in the code where indicated. I made the >> predictors here be simple normal variates, but more complex structures >> are described in the book, and in the Stata Journal article. >> >> I find synthetic models like this very useful for testing model >> assumptions. >> >> Best, Joseph Hilbe >> >> ZINB_SYN.DO >> ================================================== >> * Zero inflated Negative binomial with logit as binary component >> * Joseph Hilbe 5Jun2011 zinb_syn.do >> * LOGIT: x1=-.9, x2=-.1, _c=-.2 >> * NB2 : x1=.75, n2=-1.25, _c=2, alpha=.5 >> clear >> set obs 50000 >> set seed 1000 >> gen x1 = invnorm(runiform()) >> gen x2 = invnorm(runiform()) >> * NEGATIVE BINOMIAL- NB2 >> gen xb = 2 + 0.75*x1 - 1.25*x2 >> gen a = .5 >> gen ia = 1/a >> gen exb = exp(xb) >> gen xg = rgamma(ia, a) >> gen xbg = exb * xg >> gen nby = rpoisson(xbg) >> * BERNOULLI >> gen pi =1/(1+exp(-(.9*x1 + .1*x2+.2))) >> gen bernoulli = runiform()>pi >> gen zy = bernoulli*nby >> rename zy y >> * NB2-LOGIT HURDLE >> zinb y x1 x2, inf(x1 x2) nolog >> ================================= >> >> >> >> Zero-inflated negative binomial regression Number of obs = >> 50000 >> Nonzero obs = >> 19181 >> Zero obs = >> 30819 >> >> Inflation model = logit LR chi2(2) = >> 24712.97 >> Log likelihood = -88361.63 Prob > chi2 = >> 0.0000 >> >> ------------------------------------------------------------------------- >> >> ----- >> y | Coef. Std. Err. z P>|z| [95% Conf. >> Interval] >> -------------+----------------------------------------------------------- >> >> ----- >> y | >> x1 | .7407043 .0066552 111.30 0.000 .7276604 >> .7537483 >> x2 | -1.249479 .0067983 -183.79 0.000 -1.262804 >> -1.236155 >> _cons | 1.996782 .0069297 288.15 0.000 1.9832 >> 2.010364 >> -------------+----------------------------------------------------------- >> >> ----- >> inflate | >> x1 | .9047498 .0141011 64.16 0.000 .8771121 >> .9323875 >> x2 | .095477 .0125229 7.62 0.000 .0709326 >> .1200213 >> _cons | .2031966 .0121878 16.67 0.000 .179309 >> .2270841 >> -------------+----------------------------------------------------------- >> >> ----- >> /lnalpha | -.6778044 .0153451 -44.17 0.000 -.7078803 >> -.6477286 >> -------------+----------------------------------------------------------- >> >> ----- >> alpha | .5077305 .0077912 .4926874 >> .5232329 >> ------------------------------------------------------------------------- >> >> ----- >> >> >> >> >> >> >> >> >> >> >> > > * > * 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/

**References**:**st: RE: synthetic ZINB***From:*jhilbe@aol.com

**st: RE: synthetic ZINB***From:*jhilbe@aol.com

**Re: st: RE: synthetic ZINB***From:*Ari Samaranayaka <ari.samaranayaka@ipru.otago.ac.nz>

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