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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: MLE est. linear form with two sets of parameters |

Date |
Mon, 31 May 2010 00:52:59 -0700 (PDT) |

--- On Sun, 30/5/10, Denis Kalugin wrote: > The goal is to estimate two latent variables, let's say L1 > and L2. The variables are linked to variable y in the following > manner: > y=0 if L1<z<L2 > y=-1 if z>L2 > y=1 if z<L1 > > L1=exp(x*b1+error)/(1+exp(x*b1+error) > L2=min[exp(x*b1+error)/(1+exp(x*b1+error) > , exp(x*b2+error)/(1+exp(x*b2+error) > > This is estimated via slightly altered probit (as far as I > understand). > > P(y=-1)=norm[(log(z/(1-z)) - xb1)/sigma(error1)] > P(y=1)=(1-norm[(log(z/(1-z)) - > xb1)/sigma(error1)])*(1-norm[(log(z/(1-z)) - > xb2)/sigma(error2)]) > P(y=0)=1-p(y=-1)-p(y=1) > > However, my problem right now is how to feed TWO sets of > parameters (b1 and b2) to be estimated. If i write this > as a linear form, then stata would generate only one > vector. Looks like your are mixing up logits and probits, either use -normal- or use exp(.)/(1+exp(.)). The latter can be coded more briefly as -invlogit()- . Also note that coding normal(-xb) or invlogit(-xb) is much more stable than (1-normal(xb)) or (1-invlogit(xb)). Anyhow, for your immediate question: Your claim about how -lf- works is not correct. You really need to get your hands on: William Gould, Jeffrey Pitblado, William Sribney (2006) "Maximum Likelihood Estimation with Stata, 3rd Edition", College Station, TX: Stata Press. <http://www.stata.com/bookstore/mle.html> Your model looks very similar to -zoib-, so you can type -ssc install zoib- and look at that program. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * 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: MLE est. linear form with two sets of parameters***From:*Denis Kalugin <denis.kalugin@gmail.com>

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