GBNLPROBIT

NAME

gbnlprobit - Non linear probit regression

SYNOPSIS

gbnlprobit [options] <function definition>

DESCRIPTION

Non linear probit estimation. Minimize the negative log-likelihood

sum_{i in N_0} log(1-F(g(X_i))) + sum_{i in N_1} log(F(g(X_i)))

where N_0 and N_1 are the sets of 0 and 1 observations, g is a generic function of the independent variables and F is the normal CDF. It is also possible to minimize the score function

w_0 sum_{i in N_0} theta(F(g(X_i))-t) +

w_1 sum_{i in N_1} theta(t-F(g(X_i)))

where theta is the Heaviside function and t a threshold level. Weights w_0 and w_1 scale the contribution of the two subpopulations. The first column of data contains 0/1 entries. Successive columns are independent variables. The model is specified by a function g(x1,x2...) where x1,.. stands for the first,second .. N-th column independent variables.

options:

	-O		type of output (default 0)
	0		parameters
	1		parameters and errors
	2		<variables> and probabilities
	3		parameters and variance matrix
	4		marginal effects
	-V		variance matrix estimation (default 0)
	0		<gradF gradFˆt>
	1		< Jˆ{-1} >
	2		< Hˆ{-1} >
	3		< Hˆ{-1} J Hˆ{-1} >
	-z		take zscore (not of 0/1 dummies)
	-F		input fields separators (default " \t")
	-v		verbosity level (default 0)
	0		just results
	1		comment headers
	2		summary statistics
	3		covariance matrix
	4		minimization steps (default 10)
	5		model definition
	-g		set number of point for global optimal threshold identification
	-h		this help
	-t		set threshold value (default 0)
	0		ignore threshold
	(0,1)		user provided threshold
	1		compute optimal only global
	2		compute optimal
	-M		estimation method
	0		maximum likelihood
	1		min. score (w0=w1=1)
	2		min. score (w0=1/N0, w1=1/N1)
	-A		MLL optimization options (default 0.01,0.1,100,1e-6,1e-6,5) fields are step,tol,iter,eps,msize,algo. Empty fields for default
	step		initial step size of the searching algorithm
	tol		line search tolerance iter: maximum number of iterations
	eps		gradient tolerance : stopping criteria ||gradient||<eps
	algo		optimization methods: 0 Fletcher-Reeves, 1 Polak-Ribiere, 2 Broyden-Fletcher-Goldfarb-Shanno, 3 Steepest descent, 4 simplex
	-B		score optimization options (default 0.1,100,1e-6) fields are step,iter,msize. Empty fields for default
	step		initial step size of the searching algorithm
	iter		maximum number of iterations
	msize		max size, stopping criteria simplex dim. <max size optimization method is simplex

AUTHOR

Written by Giulio Bottazzi

REPORTING BUGS

Report bugs to <gbutils@googlegroups.com>

Package home page <http://cafim.sssup.it/˜giulio/software/gbutils/index.html>

COPYRIGHT

Copyright © 2001-2018 Giulio Bottazzi This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License (version 2) as published by the Free Software Foundation;

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.