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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Referring to coefficients after mlogit |

Date |
Mon, 15 Oct 2012 11:36:56 +0200 |

You can refer to the coefficient of the variable x1 in the first equation as [#1]_b[x1] and for the second equation: [#2]_b[x1]. Alternatively, these equations have names and you can refer to these by [name1]_b[x1] and [name2]_b[x1]. Useful can also be the -coeflegend- option present in most estimation commands, including -mlogit-. Substantively, I would simply discus the odds ratios. This avoids the whole problem, see: M.L. Buis (2010) "Stata tip 87: Interpretation of interactions in non-linear models", The Stata Journal, 10(2), pp. 305-308. Hope this helps, Maarten On Mon, Oct 15, 2012 at 11:24 AM, Korcan Kavusan <k.kavusan@tilburguniversity.edu> wrote: > Hello all, > > I am estimating a multinomial logit model with 3 possible outcomes (0, 1 , > 2). 0 is the base outcome. Mlogit gives two sets of results, one comparing > 0 and 1 and the other 0 and 2. So each independent variable has 2 > coefficients, one for each comparison. I have a difficulty in correctly > referring to these coefficients after the estimation. > > Specifically, Bowen (2010) suggests the code below to compute the value and > significance of a moderating effect for each observation. The code is > written for logit estimation. I want to adapt it to my mlogit model and do > these computations for my two sets of results, but cannot figure out how > tell the code to use the coefficient from the result set that compares 0 and > 1 (and then separately from 0-2 comparison later). It is probably a small > technical issue but for me now a real headache. I greatly appreciate any > clue. > > The original code for the logit model: > > * Estimate logit model for binary dependent variable `dismissed' > logit dismissed X1 X2 X12 > predict phat > * Define values used in computing moderating effects local xb > _b[X1]*X1+_b[X2]*X2+_b[X12]*X12+_b[_cons] > local xb0 _b[X1]*X1+_b[X2]*X2+_b[_cons] > local phat (exp(`xb')/(1+exp(`xb'))) > local phat0 (exp(`xb0')/(1+exp(`xb0'))) > gen phat0 = (exp(`xb0')/(1+exp(`xb0'))) > label var phat0 "Predicted probability (model excludes interaction > variable)" > local coef1 (_b[X1]+_b[X12]*X2) > local coef2 (_b[X2]+_b[X12]*X1) > * compute value of each moderating effect at each observation predictnl > total=`phat'*(1-`phat')*(_b[X12]+(1-2*`phat')*`coef1'*`coef2'), > se(se_total) > predictnl structural = `phat0'*(1-`phat0')*((1-2*`phat0')*_b[X1]*_b[X2]), > se(se_structural) > predictnl secondary = > `phat'*(1-`phat')*(_b[X12]+(1-2*`phat')*`coef1'*`coef2') /// > -`phat0'*(1-`phat0')*(1-2*`phat0')*_b[X1]*_b[X2], se(se_secondary) label var > total "Total Moderating Effect" > label var secondary "Secondary Moderating Effect" > label var structural "Structural Moderating Effect" > > References: > > Bowen, H. P. 2010. Testing Moderating Hypotheses in Limited Dependent > Variable and Other Nonlinear Models: Secondary Versus Total Interactions. > Journal of Management. > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/faqs/resources/statalist-faq/ > * http://www.ats.ucla.edu/stat/stata/ -- --------------------------------- Maarten L. Buis WZB Reichpietschufer 50 10785 Berlin Germany http://www.maartenbuis.nl --------------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Referring to coefficients after mlogit***From:*"Korcan Kavusan" <k.kavusan@tilburguniversity.edu>

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