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From |
Chiara Mussida <cmussida@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: mlogit coefs |

Date |
Fri, 20 Apr 2012 15:00:51 +0200 |

Thanks for all suggestions. I checked my dataset containing all the employed covariates: mlogit transition male_unmarried female_married female_unmarried age agesq ncomp child northw northe centre Ubenef edu1 edu2 health qu1nolav qu3nolav qu2nolav nopersincnolav noothersineq qu1ot qu2ot qu3ot if age>=15 & age<=64, b(3) the dummy indicators for quantile of non labour income "qu1nolav qu3nolav qu2nolav" takes the value one only for few individuals n each subsample of analysis. e.g. 9 indivv in qu1nolav for the first outcome (transition==1), etc. I'm going to try to substitute the indicators for the quantiles with a dummy variable for the presence absence of non labour income. My question is more general: Is it possible that specific dummy variables that take the value 1 for few few indviduals do gen not robust results? I mean The above mlogit results differ to the ones obtained on only a subsample of the initial sample (e.g. 3 transitions instead of 9). I tried to re-estimate the model without the specific dummies quoted, and the results semm to be robust to the two alternative model specifications mentioned. Thanks Chiara On 17/04/2012, David Hoaglin <dchoaglin@gmail.com> wrote: > Dear Chiara, > > I have a comment, much more minor than Maarten's, but still useful. > > If the contribution of age is nonlinear, it may not be satisfactory to > assume that the nonlinearity is quadratic (in practice it often is > not). You did not mention the number of observations; but since you > have the entire labor force, you may have enough data to approach the > functional form of age empirically. One strategy would separate the > values of age into disjoint intervals (as narrow as the data will > support), include in the model a dummy variable for each interval > except one, and plot the fitted coefficients of those dummy variables > against the midpoints of the intervals. If that plot looks quadratic, > fine. But it may suggest that a linear spline would be a better > summary of the contribution of age (taking into account the other > variables in the model). > > David Hoaglin > > On Tue, Apr 17, 2012 at 10:00 AM, Chiara Mussida <cmussida@gmail.com> wrote: >> Dear All, >> I run a mlogit model for 9 labour market outcomes (transitions between >> the three states of employment unemployment and inactivity, therefore >> 6 transitions and 3 permanences), like: >> >> mlogit transition male_unmarried female_married female_unmarried age >> agesq ncomp child northw northe centre Ubenef edu1 edu2 health >> qu1nolav qu3nolav qu2nolav nopersincnolav noothersineq qu1ot qu2ot >> qu3ot if age>=15 & age<=64, b(3) > * > * 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/ > -- Chiara Mussida PhD candidate Doctoral school of Economic Policy Catholic University, Piacenza (Italy) * * 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/

**Follow-Ups**:**Re: st: mlogit coefs***From:*Chiara Mussida <cmussida@gmail.com>

**References**:**st: mlogit coefs***From:*Chiara Mussida <cmussida@gmail.com>

**Re: st: mlogit coefs***From:*David Hoaglin <dchoaglin@gmail.com>

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