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gbnlprobit - Online in the Cloud

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This is the command gbnlprobit that can be run in the OnWorks free hosting provider using one of our multiple free online workstations such as Ubuntu Online, Fedora Online, Windows online emulator or MAC OS online emulator

PROGRAM:

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

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