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gbnlpolyit - Non linear polyit regression


gbnlpolyit [options] <function definition>


Non linear polyit estimation. Minimize the negative log-likelihood

sum_{h=0}^{L-1} log(A+h) - sum_{l=1}^L sum_{h=0}^{n_l-1} log(a_l+h)

for the Polya specification or

L log(A) - sum_{l=1}^L n_l log(a_l)

for the multinomial specification, where A = sum_{l=1}^L a_l and L is the number of
alternatives. The input data file should contain L rows, one for each alternative, of the
type n x1 ... XN. The first column contains the dependent variable (# of observations) and
the other columns the independent variables. The model is specified by a function
a_l=g(x1,x2...) where x1,.. XN stands for the first, second .. N-th column of independent


-O type of output (default 0)

0 parameters and log-like (ll)

1 marginal effects

2 marginal elasticities

3 n_l n*_l a*_l *=estimated

4 occupancies classes

-F input fields separators (default " \t")

-V standard errors and p-scores of diff. from zero using bootstrap

-r number of replicas (default 20)

-v verbosity level (default 0)

0 just results

1 comment headers

2 summary statistics

3 covariance matrix

4 minimization steps

5 model definition

-R set the rng seed (default 0)

-M set the model to use (default 0) |

0 Polya

1 multinomial

-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, 5

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