start_params : array (optional)
starting value for parameters ub minimization. If None then
fitstart method is called for the starting values.
maxiter : int or ‘cue’
Number of iterations in iterated GMM. The onestep estimate can be
obtained with maxiter=0 or 1. If maxiter is large, then the
iteration will stop either at maxiter or on convergence of the
parameters (TODO: no options for convergence criteria yet.)
If maxiter == ‘cue’, the the continuously updated GMM is
calculated which updates the weight matrix during the minimization
of the GMM objective function. The CUE estimation uses the onestep
parameters as starting values.
inv_weights : None or ndarray
inverse of the starting weighting matrix. If inv_weights are not
given then the method start_weights is used which depends on
the subclass, for IV subclasses inv_weights = z’z where z are
the instruments, otherwise an identity matrix is used.
weights_method : string, defines method for robust
Options here are similar to statsmodels.stats.robust_covariance
default is heteroscedasticity consistent, HC0
currently available methods are
- cov : HC0, optionally with degrees of freedom correction
- hac :
- iid : untested, only for Z*u case, IV cases with u as error indep of Z
- ac : not available yet
- cluster : not connected yet
- others from robust_covariance
wargs` : tuple or dict,
required and optional arguments for weights_method
- centered : bool,
indicates whether moments are centered for the calculation of the weights
and covariance matrix, applies to all weight_methods
- ddof : int
degrees of freedom correction, applies currently only to cov
- maxlag : int
number of lags to include in HAC calculation , applies only to hac
- others not yet, e.g. groups for cluster robust
has_optimal_weights: If true, then the calculation of the covariance :
matrix assumes that we have optimal GMM with .
Default is True.
TODO: do we want to have a different default after onestep?
optim_method : string, default is ‘bfgs’
numerical optimization method. Currently not all optimizers that
are available in LikelihoodModels are connected.
optim_args : dict
keyword arguments for the numerical optimizer.