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 (ndarray) – 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 (ndarray) – estimated parameters

• weights (ndarray) – optimal weighting matrix calculated with final parameter estimates

Notes