st: Problem: Mlogit: Variance Matrix nonsymmetric after bsample

[jacmuell@uni-potsdam.de]

Dear Statalist,

I would greatly appreciate your help.

I am currently trying predict the contrafactual probability of  
nonvoters to vote for a certain party. I am using a mlogit-model with  
interaction effects and difficult technique estimations options. To  
get more robust results, I want bootstrapped predictions using bsample.
For some reason, if using bsample, after about 5 repetitions with the  
random samples, the mlogit-model isn't concave anymore, but apparently  
still converges. There's a warning message saying Variance Matrix  
nonsymmetric or highly singular. This results in very strange  
coefficients, which are still predicted und thus ruin the results.
I read that this might be due to the fact that there are not enough  
cases with some sparse indicator variables. Therefore i tried reducing  
the categories in some variables, with no effect. Using the total  
option to see which variables produce empty cells doesn't help either.

I therefore used capture and e(p) so only regression samples w/  
meaningful coeff. are included. This shows that only the first seven  
examples are meaningful. How can that be and what to do about it?

Thank you very much!

Here the code used (stata 10):

preserve
set seed 731


	forv i = 1/200 {
				bsample, strata(Wä)

		// Loop over dates and surveys


				// Estimate model
		capture mlogit party _I* [pweight=wei_ipfges_2] if party <= 5,  
difficult technique(nr 15 bhhh 15 dfp 15 bfgs 15) base(1)

		if e(p)~= . {

			// Predict
			predict Phat1 Phat2 Phat3 Phat4 Phat5 if Wä == 0

			// Post
			forv j = 1/5 {
				sum Phat`j' [aw=wei_ipfges_2] if Wä==0, meanonly
				post `CI' (`j') (r(mean))
			}

					drop Phat*
				}
			}

	restore,preserve


postclose `CI'

exit


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