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From |
"Austin Nichols" <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Re: NLogit Post estimation |

Date |
Fri, 27 Jun 2008 21:26:13 -0400 |

Caroline B. Theoharides <cbtheo@gmail.com>: There are several ways to do this depending on what kind of "marginal effects" you want, and the syntax is given by -help nlogit postestimation-. Perhaps the most natural approach is to simply replace the value of kids with each of the possible values at which you want to calculate predictions (i.e. each of your counterfactuals) and predict probabilities. This uses the empirical distribution of each choice's attributes, but I believe -spost- uses means of the estimation sample instead (or would if it supported -nlogit-). The two approaches are illustrated below. The second approach assumes every restaurant has the same (mean) cost, distance, and rating, which is why I say the first approach is more natural; others may disagree with that assessment. The calculation of marginal effects will depend on your counterfactuals. clear all webuse restaurant nlogitgen type=restaurant(fast: 1 | 2, family: 3 | 4 | 5, fancy:6 | 7) nlogit ch cost d ra||type:income kids,base(family)||re:,noconst case(f) preserve qui forv i=0/7 { replace kids=`i' predict p`i'_*, pr forv r=1/7 { su p`i'_2 if re==`r', meanonly loc k`i'r`r'=r(mean) } } loc l forv r=1/7 { forv k=0/7 { loc l `l' `k`k'r`r'' `=`r'.`k'' } } loc o "recast(spike) yti(Prob) xti(Rest) yla(0(.1).5) xla(" forv r=1/7 { loc o `"`o' `r'.4 "`:label (re) `r''" "' } loc o `"`o', angle(90))"' tw scatteri `l', `o' name(e, replace) bys type rest: su p*_2 restore foreach v in cost d ra income { su `v', meanonly loc `v'=r(mean) } preserve collapse ch cost d ra income kids fam, by(type rest) foreach v in cost d ra income { qui replace `v'=``v'' } expand 8 bys type rest: replace kids=_n-1 bys type rest: replace fa=_n predict p*, pr loc l2 forv r=1/7 { forv k=0/7 { su p2 if rest==`r' & kids==`k', meanonly loc l2 `l2' `r(mean)' `=`r'.`k'' } } tw scatteri `l2', `o' name(m, replace) sort kids type rest li type rest kids p2, noo sepby(kids) restore gr combine e m, ycommon nocopies ti(Pr Choice by Number of Kids) On Fri, Jun 27, 2008 at 9:22 AM, Caroline B. Theoharides <cbtheo@gmail.com> wrote: > To clarify the question somewhat, an explicit example may be useful. > In the context of the following nested logit model, we are trying to > generate predicted probabilities of selecting each restaurant (7 > different outcomes) for each value of kids (i.e., kids=0, kids=1, > kids=2, etc.). > webuse restaurant, clear > nlogitgen type=restaurant(fast: 1 | 2, family: 3 | 4 | 5, fancy:6 | 7) > nlogittree restaurant type, choice(chosen) > nlogit chosen cost distance rating || type: income kids, > base(family)|| restaurant:, noconst case(family_id) > > Thanks for any suggestions. > > Caroline > > On 6/24/08, Caroline B. Theoharides <cbtheo@gmail.com> wrote: >> >> >> Hello, >> >> I am running a 2 level nested logit model in Stata 10 using –nlogit-. Does >> anyone know how to calculate marginal effects with the –nlogit- command? >> >> I specifically want to do post-estimation analysis of the predicted >> probabilities, similar to that encompassed by –asprvalue- in –spost- (which >> incidentally doesn't support nlogit). Is it possible to calculate these >> predicted probabilities in Stata? >> >> >> Thank you, >> Caroline * * 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/

**References**:**st: Re: NLogit Post estimation***From:*"Caroline B. Theoharides" <cbtheo@gmail.com>

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