[R] Different standard errors from R and other software

[Joris Meys]

If I understand correctly from their website, "discrete choice" models
are mostly generalized linear models with the common link functions
for discrete data? Apart from a few names I didn't recognize, all
analyses seem quite "standard" to me. So I wonder why you would write
the log-likelihood yourself for techniques that are implemented in R.

Unless I missed something pretty important, or you want to do a
specific analysis that wasn't clear to me, you should take a closer
look at the possibilities in R for generalized linear (mixed)
modelling and so on.

Binary choice translates to a simple glm with a logit function.
Multinomial choice can be done with eg. multinom() from nnet. Ordered
choice can be done with polr() from the MASS package. A nice one to
look at is the package mgcv or gamm4 in case of big datasets. They
offer very flexible models that can include random terms, specific
variance-covariance structures and non-linear relations in the form of
splines.

Apologies if this is all obvious and known to you. In that case you
might want to specify what exactly it is you are comparing and how
exactly you calculated it yourself.

Cheers
Joris

On Fri, Jun 25, 2010 at 11:47 PM, Min Chen <chenmin0905 at gmail.com> wrote:
> Hi all,
>
>    Sorry to bother you. I'm estimating a discrete choice model in R using
> the maxBFGS command. Since I wrote the log-likelihood myself, in order to
> double check, I run the same model in Limdep. It turns out that the
> coefficient estimates are quite close; however, the standard errors are very
> different. I also computed the hessian and outer product of the gradients in
> R using the numDeriv package, but the results are still very different from
> those in Limdep. Is it the routine to compute the inverse hessian that
> causes the difference? Thank you very much!
>
>     Best wishes.
>
>
> Min
>
>
> --
> Min Chen
> Ph.D. Candidate
> Department of Agricultural, Food, and Resource Economics
> 125 Cook Hall
> Michigan State University
>
>        [[alternative HTML version deleted]]
>
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-- 
Joris Meys
Statistical consultant

Ghent University
Faculty of Bioscience Engineering
Department of Applied mathematics, biometrics and process control

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