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

To |
[email protected] |

Subject |
Re: st: Interesting (?) -ml- question |

Date |
Tue, 07 Sep 2004 13:02:57 -0500 |

Mark Schaffer <[email protected]> asks about what appear to be multiple offset variables with a single equation in -ml-: > The likelihood function that I want to minimize has the form of, say, one > dependent variable y and 3 independent variables w, x and z. However, of > the independent variables, only x has a coefficient associated with it. > The other two variables, w and z, are needed for calculating the > likelihood but don't have coefficients. > > My question - what's the best way of passing w and z to the likelihood > function? > > I can think of 3 ways, but all seem pretty hacky: > > 1. Save "w z" as a string in a global macro called, say, MyMLvars. The > likelihood program mymlprog knows to look in $MyMLvars to find them. > > 2. In the ml model command, list w and z as additional endogenous > variables, e.g., > > ml model d0 mymlprog (y w z = x) > > Then mymlprog can find w and z as $ML_y2 and $ML_y3. > > 3. The hackiest of all - list w and z as indep vars, but constrain the > coeffs to be zero: > > constraint 1 w=0 > constraint 2 z=0 > ml model d0 mymlprog (y = x w z) > > Is there a better way? Or, if there isn't, which of these is to be > preferred? This will depend upon the form of the likelihood, but if the variables -w- and -z- are part of the likelihood but are not necessarily part of -xb- for the first equation, I would most definitely use constraints and extra equations. Here is an overly simple example: ***** BEGIN: clear program drop _all program myll version 8.2 args lnf xb1 xb2 xb3 quietly replace `lnf' = - ($ML_y1-`xb1')^2 - (`xb2')^2 - (`xb3')^2 end global xx sysuse auto const 1 [eq2]trunk = 1 const 2 [eq3]displacement = 1 ml model lf myll /// (eq1: mpg = turn) /// (eq2: trunk, noconstant) /// (eq3: displ, noconstant) /// , constr(1 2) max ml display, first ***** END: Here are the results from the -ml display-: ***** BEGIN: initial: log likelihood = -36008 alternative: log likelihood = -34482.631 rescale: log likelihood = -23753.635 rescale eq: log likelihood = -4520 Iteration 0: log likelihood = -3516088 Iteration 1: log likelihood = -3512747.6 Iteration 2: log likelihood = -3512747.6 . ml display, first Number of obs = 74 Wald chi2(1) = 2527.66 Log likelihood = -3512747.6 Prob > chi2 = 0.0000 ( 1) [eq2]trunk = 1 ( 2) [eq3]displacement = 1 ------------------------------------------------------------------------------ mpg | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- turn | -.9457877 .018812 -50.28 0.000 -.9826586 -.9089169 _cons | 58.7965 .7503857 78.36 0.000 57.32577 60.26723 ------------------------------------------------------------------------------ ***** END: --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/

**Follow-Ups**:**Re: st: Interesting (?) -ml- question***From:*Mark Schaffer <[email protected]>

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