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From |
Dorothy Bridges <dbstata@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Inequality constraint with mata -optimize- |

Date |
Mon, 30 May 2011 08:09:48 -0700 |

Thanks, Stas. Unfortunately, with the model parameterized as-is, I do indeed get negative variance estimates (!). Parameterizing as the square of something leads to very unexpected (almost certainly wrong) estimates of the other parameters. In MATLAB, simply imposing an inequality constraint seems to "work" .... I realize that there's a problem with the specification of the model, but it would still be nice to try something like Cp'>c. On Sun, May 29, 2011 at 7:44 PM, Stas Kolenikov <skolenik@gmail.com> wrote: > The log won't quite do the trick, as it does not allow the variance to > be zero. Square will; you can parameterize the variance as a square of > something. However, it would produce (at least) two problems. One is > that when the true value is at zero, the Jacobian is degenerate > (there's a row/column corresponding to that square, say variance = > b*b, of which the derivative is 2*b, and if b is zero, the whole > column is zero), which screws up both convergence and inference. > Another problem is that when your model is specified so badly that the > population "variance" is negative (it does happen in some models, > although I am not familiar with the model Dorothy mentioned). If this > happens, the MLE converges to zero producing the above problem, but > also you may not see the misspecification really occurring as you did > not allow zero values. So it's a two-edged sword; I would just > parameterize the model with variance as is, and see if you need to > worry about negative estimates (for which you still can have > asymptotically appropriate inference with -robust- standard errors). > > On Sun, May 29, 2011 at 7:21 PM, Richard Williams > <richardwilliams.ndu@gmail.com> wrote: >> At 06:09 PM 5/29/2011, Dorothy Bridges wrote: >>> >>> Thanks, Stas. I am using ML to estimate a disequilibrium model, in >>> which the likelihood function is set up as in Maddala and Nelson, >>> "Maximum Likelihood Methods for Models of Markets in Disequilibrium," >>> Econometrica, 1974. I simply want to constrain two of the parameters >>> -- variances of the error terms in the demand and supply equations -- >>> to be greater than or equal to zero. >> >> Often this is done by estimating the log of the parameter. See, for example, >> Stata's -hetprob- program. >> >> >> ------------------------------------------- >> Richard Williams, Notre Dame Dept of Sociology >> OFFICE: (574)631-6668, (574)631-6463 >> HOME: (574)289-5227 >> EMAIL: Richard.A.Williams.5@ND.Edu >> WWW: http://www.nd.edu/~rwilliam >> >> * >> * 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/ >> > > > > -- > Stas Kolenikov, also found at http://stas.kolenikov.name > Small print: I use this email account for mailing lists only. > > * > * 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: Inequality constraint with mata -optimize-***From:*Dorothy Bridges <dbstata@gmail.com>

**Re: st: Inequality constraint with mata -optimize-***From:*Stas Kolenikov <skolenik@gmail.com>

**Re: st: Inequality constraint with mata -optimize-***From:*Dorothy Bridges <dbstata@gmail.com>

**Re: st: Inequality constraint with mata -optimize-***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: Inequality constraint with mata -optimize-***From:*Stas Kolenikov <skolenik@gmail.com>

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