statsmodels.sandbox.regression.gmm.LinearIVGMM.fit¶
- LinearIVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None)¶
Estimate parameters using GMM and return GMMResults
TODO: weight and covariance arguments still need to be made consistent with similar options in other models, see RegressionResult.get_robustcov_results
- Parameters
- 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
orndarray
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
str
,defines
method
for
robust
Options here are similar to
statsmodels.stats.robust_covariance
default is heteroscedasticity consistent, HC0currently 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 \(W = S^{-1}\). Default is True. TODO: do we want to have a different default after onestep?
- optim_method
str
,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.
- start_params
- Returns
- results
instance
of
GMMResults
this is also attached as attribute results
- results
Notes
Warning: One-step estimation, maxiter either 0 or 1, still has problems (at least compared to Stata’s gmm). By default it uses a heteroscedasticity robust covariance matrix, but uses the assumption that the weight matrix is optimal. See options for cov_params in the results instance.
The same options as for weight matrix also apply to the calculation of the estimate of the covariance matrix of the parameter estimates.