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:

startndarray

starting value for parameters

maxiterint

maximum number of iterations

start_weightsarray (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:

paramsndarray

estimated parameters

weightsndarray

optimal weighting matrix calculated with final parameter estimates