statsmodels.sandbox.regression.gmm.NonlinearIVGMM.fititer

NonlinearIVGMM.fititer(start, maxiter=2, start_invweights=None, weights_method='cov', wargs=(), optim_method='bfgs', optim_args=None)

iterative estimation with updating of optimal weighting matrix

stopping criteria are maxiter or change in parameter estimate less than self.epsilon_iter, with default 1e-6.

Parameters:

start : array

starting value for parameters

maxiter : int

maximum number of iterations

start_weights : array (nmoms, nmoms)

initial weighting matrix; if None, then the identity matrix is used

weights_method : {‘cov’, ...}

method to use to estimate the optimal weighting matrix, see calc_weightmatrix for details

Returns:

params : array

estimated parameters

weights : array

optimal weighting matrix calculated with final parameter estimates