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From |
Christopher Baum <[email protected]> |

To |
"[email protected]" <[email protected]> |

Subject |
Re: st: nonlinear maximum likelihood with ml |

Date |
Tue, 3 May 2011 07:04:37 -0400 |

<> On May 3, 2011, at 2:33 AM, Tatyana wrote: > Is there any way to use "ml" to estimate a non-linear equation? I'm > trying to estimate the parameters of the distribution of an outcome > (Y) assuming that Y follows a Beta distribution with parameters that > depend on some X's. It's easy to write down the log likelihood > function, but the equation for Y is non-linear. Equations to be estimated with -ml- are almost always nonlinear: even in the case of using -ml- to estimate a linear regression. It can just as well estimate a nonlinear regression, or a model involving other distributions such as you suggest above. But before reinventing the wheel, check out the available 'canned' routines in this area, such as -betafit-, -betaprior-, -dagfit-, -gb2fit-, -gbgfit- from SSC, all of which show up with -findit beta-. Kit Kit Baum | Boston College Economics & DIW Berlin | http://ideas.repec.org/e/pba1.html An Introduction to Stata Programming | http://www.stata-press.com/books/isp.html An Introduction to Modern Econometrics Using Stata | http://www.stata-press.com/books/imeus.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/

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