[Limdep Nlogit List] Getting standard errors for omitted variables?

[Jason Ong]

thanks Bill for the prompt response
Sorry I should have clarified that this is effects coded.
so my coefficient for my reference level (say for the first attribute) will
be -1* (0.4 + (-0.5) + (-1.3)) = 1.4
is there any way to calculate the standard error around this 1.4 value?


*Jason Ong*
Twitter: @DrJasonJOng
PhD, MMed, MBBS, FAChSHM, FRACGP

Sexual Health Physician, Melbourne Sexual Health Centre, Alfred Health
Associate Professor (Hon), London School of Hygiene and Tropical Medicine,
UK
Central Clinical School, Monash University, Australia
Melbourne School of Population and Global Health, University of Melbourne,
Australia
Associate Editor, Sexually Transmitted Infections
Special Issues Editor, Sexual Health
Board Director, ASHM (https://protect-au.mimecast.com/s/ejWVCVAGXPtWyL8guGGc2v?domain=ashm.org.au)
https://protect-au.mimecast.com/s/zfnYCWLJY7iMqg8DuxIWvs?domain=lshtm.ac.uk
https://protect-au.mimecast.com/s/s6KnCXLKZoi8KyZOIDVyRD?domain=researchgate.net

If you are more fortunate than others, build a longer table, not a taller
fence.



On Fri, Jan 3, 2020 at 12:13 PM William Greene via Limdep <
limdep at mailman.sydney.edu.au> wrote:

> Jason.  The coefficient on the base level is zero.  There is no standard
> error for
> it.  The zero is a fixed value, not an estimated coefficient.
> /Bill Greene
>
> On Thu, Jan 2, 2020 at 7:21 PM Jason Ong via Limdep <
> limdep at mailman.sydney.edu.au> wrote:
>
> > hi,
> >
> > When I run an MNL model, it gives me outputs of the coefficients for each
> > attribute level
> > However, I would also like to get the standard errors (to calculate the
> 95%
> > CI) for the omitted/reference level
> > how could I calculate this?
> > thank you kindly for your help
> >
> > Discrete choice (multinomial logit) model
> >
> > Dependent variable               Choice
> >
> > Log likelihood function     -3332.83194
> >
> > Estimation based on N =   7008, K =  14
> >
> > Inf.Cr.AIC  =   6693.7 AIC/N =     .955
> >
> > ---------------------------------------
> >
> >             Log likelihood R-sqrd R2Adj
> >
> > ASCs  only  model must be fit separately
> >
> >                Use NLOGIT ;...;RHS=ONE$
> >
> > Note: R-sqrd = 1 - logL/Logl(constants)
> >
> > Warning:  Model does not contain a full
> >
> > set of ASCs. R-sqrd is problematic. Use
> >
> > model setup with ;RHS=one to get LogL0.
> >
> > ---------------------------------------
> >
> > Response data are given as ind. choices
> >
> > Number of obs.=  7008, skipped    0 obs
> >
> >
> >
> --------+--------------------------------------------------------------------
> >
> >         |                  Standard            Prob.      95% Confidence
> >
> >  CHOICEV|  Coefficient       Error       z    |z|>Z*         Interval
> >
> >
> >
> --------+--------------------------------------------------------------------
> >
> >    C20|1|     .40410***      .03059    13.21  .0000      .34413    .46406
> >
> >    C40|1|    -.46829***      .03070   -15.26  .0000     -.52845   -.40813
> >
> >    C60|1|   -1.26084***      .03594   -35.08  .0000    -1.33128  -1.19040
> >
> > ACVEND|1|    -.09467**       .04822    -1.96  .0496     -.18918   -.00017
> >
> > ACSHEL|1|     .45141***      .05013     9.00  .0000      .35315    .54967
> >
> >  ACMED|1|     .08191*        .04740     1.73  .0840     -.01099    .17480
> >
> > ACPHAR|1|     .05906         .05280     1.12  .2633     -.04443    .16255
> >
> >  ACCBO|1|    -.13720***      .05293    -2.59  .0095     -.24095   -.03345
> >
> > ACSOPV|1|    -.81479***      .05596   -14.56  .0000     -.92448   -.70510
> >
> > PLARBR|1|    -.10541***      .03289    -3.21  .0013     -.16986   -.04095
> >
> >  PSMPL|1|     .04375         .03167     1.38  .1671     -.01832    .10582
> >
> >  PSMBR|1|     .03257         .03254     1.00  .3168     -.03120    .09635
> >
> > INFVID|1|    -.06273**       .02688    -2.33  .0196     -.11540   -.01005
> >
> > INFCHA|1|    -.05929**       .02529    -2.34  .0191     -.10886   -.00972
> >
> >
> >
> --------+--------------------------------------------------------------------
> >
> >
> > *Jason Ong*
> > Twitter: @DrJasonJOng
> > PhD, MMed, MBBS, FAChSHM, FRACGP
> >
> > Sexual Health Physician, Melbourne Sexual Health Centre, Alfred Health
> > Associate Professor (Hon), London School of Hygiene and Tropical
> Medicine,
> > UK
> > Central Clinical School, Monash University, Australia
> > Melbourne School of Population and Global Health, University of
> Melbourne,
> > Australia
> > Associate Editor, Sexually Transmitted Infections
> > Special Issues Editor, Sexual Health
> > Board Director, ASHM (https://protect-au.mimecast.com/s/Q1DBCYWL1vinlJXASVPz8X?domain=ashm.org.au)
> > https://protect-au.mimecast.com/s/zfnYCWLJY7iMqg8DuxIWvs?domain=lshtm.ac.uk
> > https://protect-au.mimecast.com/s/s6KnCXLKZoi8KyZOIDVyRD?domain=researchgate.net
> >
> > If you are more fortunate than others, build a longer table, not a taller
> > fence.
> > _______________________________________________
> > Limdep site list
> > Limdep at mailman.sydney.edu.au
> > http://limdep.itls.usyd.edu.au
> >
> >
>
> --
> William Greene
> Department of Economics, emeritus
> Stern School of Business, New York University
> 44 West 4 St.
> New York, NY, 10012
> URL: https://protect-au.mimecast.com/s/IkZyCZYM2VFkgVYQsxTtr3?domain=people.stern.nyu.edu
> Email: wgreene at stern.nyu.edu
> Ph. +1.646.596.3296
> Editor in Chief: Journal of Productivity Analysis
> Editor in Chief: Foundations and Trends in Econometrics
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> Associate Editor: Journal of Business and Economic Statistics
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