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 gradF^t>
- 1
-
< J^{-1} >
- 2
-
< H^{-1} >
- 3
-
< H^{-1} J H^{-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 <[email protected]>
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.