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From |
[email protected] (Jeff Pitblado, StataCorp LP) |

To |
[email protected] |

Subject |
Re: st: ml check shows errors but ml maximize works |

Date |
Thu, 26 May 2005 15:24:50 -0500 |

Alita Nandi <[email protected]> was getting an error message from -ml check- when -ml max- would otherwise produce estimation results. Alita privately sent me her likelihood evaluator and a couple of log files. After looking at the log files and playing with the likelihood evaluator we have determined that all is well with -ml max- and -ml check-. -ml check- tests various things about your likelihood evaluator. One of which is that a different likelihood value results when the coefficient vector is changed. This is where -ml check- exited with an error for Alita. Alita's evaluator looks like it calculates the likelihood for something like a Heckman model (see [R] heckman). This model has 4 equations, the last two equations estimate "sigma" and "rho". As programmed, Alita's likelihood evaluator requires non-negative values for the "sigma" equation and the "rho" equation requires values between -1 and 1; otherwise, it results in missing values for the log likelihood. By default, -ml- initially supplies your likelihood evaluator with a vector of zeroes. If the resulting log likelihood value is not missing, -ml- starts optimizing; otherwise, -ml- replaces the starting values with random numbers until it gets a nonmissing value for the log likelihood or runs out of attempts (which is about 1000 attempts). This usually doesn't cause a problem, except when one of the equations has a severely restricted range of feasible values such as the "rho" equation. We mention restricted ranges in a 'Technical Note' of the book on -ml-, page 56. Here is a partial quote, where we are talking about an -ml- version of the linear regression model: The way we have parameterized this model, the log likelihood is defined only for sigma>0. -ml- is designed to deal with restricted ranges--there is nothing special you need do--but you should be impressed and realize that you are asking a lot of -ml-. There could be convergence problems, and even if there are not, -ml- is going to perform more work, so the maximization process may not occur as quickly as it otherwise could. It would be better if the model were parameterized so that [the entire real line] is a valid range for each element of [the coefficient vector] We should add the caveat lector at the end of the above quoted first paragraph: ... quickly as it otherwise could. Note that -ml- may not be able to find feasible initial values for restricted ranges such as (-1,1) or (0,1). It would be better ... Reference: Gould, W., J. Pitblado, and W. Sribney. 2003. Maximum Likelihood Estimation with Stata, 2nd edition. College Station, TX: Stata Press. Al Feiveson <[email protected]> pointed out that using the ML_ic global macro in the likelihood evaluator could cause -ml check- to have similar problems as described above by Alita. > I encountered this when I referred to the iteration counter $ML_ic in my > .ado program. Apparently this global doesn't exist at -ml check- time. > However when I run the program without the -ml check- command it worked. I > don't know if this is the sort of thing that Alita had in her program. Al is correct, the ML_ic global macro is created by -ml-'s optimization routine that loops until convergence; however, -ml check- does not use -ml-'s optimization routine to call the likelihood evaluator. --Jeff [email protected] * * 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/

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