How to validate a Multinomial Logit and Probit Model fit? I would like to know how do you determine the performance of your models. That is, if you fit a multinomial logit or probit model for un-ordered discrete choice. What do you use to evaluate whether you have a good model?  
Please provide me with any reference material that I could read more on this topic. Websites, articles or book references on this topic will be a great help. I am kind of stuck! 
I have R, SAS, Stata, SPSS, and Minitab available.  
 A: I would consider these tests at a minimum:


*

*Hausman or Small-Hsiao tests of the IIA 

*Confusion matrices of predicted vs. actual outcome

*Information criteria (AIC, BIC) 

*Various scalar measures of fit (like McFadden's $R^2$)

*Wald or LR tests for combining alternatives


With Stata, check out the SPost from Long and Freese as their as their categorical variables book for code and a nice intro to all of these tests with examples.
A: I like going through the tutorials on IDRE. Here are the R ones, they have them for SAS as well:
http://www.ats.ucla.edu/stat/r/dae/logit.htm
http://www.ats.ucla.edu/stat/r/dae/mlogit.htm
http://www.ats.ucla.edu/stat/r/dae/probit.htm
Here is what the multinomial article says about diagnostics:

Diagnostics and model fit: Unlike logistic regression where there are
  many statistics for performing model diagnostics, it is not as
  straightforward to do diagnostics with multinomial logistic regression
  models. For the purpose of detecting outliers or influential data
  points, one can run separate logit models and use the diagnostics
  tools on each model.

From the logistic article:

Diagnostics: The diagnostics for logistic regression are different
  from those for OLS regression. For a discussion of model diagnostics
  for logistic regression, see Hosmer and Lemeshow (2000, Chapter 5).
  Note that diagnostics done for logistic regression are similar to
  those done for probit regression.

A: according to Mc Fadden the rule of thumb is that pseudo r2 should be between .20 and .40 for mnl. Am also having problems with the same, you can also use pearson and deviance if using SPSS, the chi2 value should not be significant.
