st: ML estimation of bivariate ordered probit models with endogeneity

["Zurab Sajaia" <zsajaia@hotmail.com>]

Dear all,

I'd like to share with you the new program -bioprobit- : maximum-likelihood 
estimation of bivariate ordered probit models. The setup is more ore less 
straightforward generalization of univariate ordered probit (or bivariate 
probit):

we have latent variables
y*_1=x_1*b_1+e_1
y*_2=x_2*b_2+e_2
where corr(e_1, e_2)=rho

observed are y_1=1...J, y_2=2...K (in fact program works with any coding of 
dependent variables not just from 1 to J or K).

If -endogenous- option is used, a modified model
y*_1=x_1*b_1+e_1
y*_2=gamma*y*_1+x_2*b_2+e_2

will be estimated ("standard" identification requirements apply).


-bioprobit- estimates all betas, rho, cutoff points and gamma. it is 
implemented as a d2 procedure.

The program and an accompanying paper was submitted to Stata Journal. The 
paper gives more detailed description of the model and also presents results 
of Monte Carlo simulations. You can find a draft version at 
http://econ.worldbank.org/programs/poverty/toolkit#bioprobit

Program can be installed by:

.net install bioprobit, 
from(http://siteresources.worldbank.org/INTPOVRES/Resources)
note: since Stata is case-sensitive you'll need to type the url as given 
here.


by typing :
. net from http://siteresources.worldbank.org/INTPOVRES/Resources

you'll find some programs written by us on poverty and inequality analysis, 
and other useful tools, including -xml_tab- module to export Stata output to 
a printer-ready formatted tables in an xml file directly openable in MS 
Excel and OpenOffice.org Calc;

recent addition to the site is program -bestreg- that implements the Leaps 
and Bounds algorithm for best subset selection for the regression analysis. 
It's still in the beta version but since there was some interest in tools 
for all possible regressions on Statalist recently I decided to put it 
online as well.


And of course, I'd appreciate any comment or suggestion.
Zurab


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