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From |
"Nick Cox" <[email protected]> |

To |
<[email protected]> |

Subject |
st: RE: simulated maximum likelihood estimation |

Date |
Mon, 9 May 2005 01:11:33 +0100 |

I am not sure exactly what you are trying to do here. It is usually better to set up a log likelihood function directly. What you are logging here does not have the usual form of a likelihood function. Nick [email protected] Jun Xu > I am not sure if some expert could give me some hint about > where to go. Here > I am trying to estimate a logit with an added expotential > distributed random > error (with mean of 5). I am using simualted mle with 100 > replications, but > cannot get it converged. Not sure if I had a correct setup. > > > set trace off > set more off > cap program drop my_ll > program my_ll > version 8.2 > args lnf xb > tempname b > sca `b' = 5 > > forval j = 1/100 { > tempname ed`j' > } > > qui replace `lnf' = 0 > forval i = 1/100 { > qui gen double `ed`i'' = -ln(uniform())*`b' > qui replace `lnf'= `lnf' + invlogit( `xb'+`ed`i'') > if $ML_y1 == > 1 > qui replace `lnf'= `lnf' + invlogit(-(`xb'+`ed`i'')) > if $ML_y1 == > 0 > } > > qui replace `lnf' = ln(`lnf') > end > > use binlfp2.dta, clear > ml model lf my_ll (lfp = k5 k618 age), technique(nr bhhh dfp bfgs) > ml maximize > > > initial: log likelihood = 2948.488 > alternative: log likelihood = 2952.4599 > rescale: log likelihood = 2952.4599 > (setting optimization to Newton-Raphson) > numerical derivatives are approximate > flat or discontinuous region encountered > Iteration 0: log likelihood = 2949.0411 (not concave) > numerical derivatives are approximate > flat or discontinuous region encountered > Iteration 1: log likelihood = 2950.7797 (not concave) > numerical derivatives are approximate > flat or discontinuous region encountered > no observations > r(2000); > * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**RE: st: RE: simulated maximum likelihood estimation***From:*"Jun Xu" <[email protected]>

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