statsmodels.sandbox.regression.gmm.IVGMM.fit
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IVGMM.fit(start_params=None, maxiter=10, inv_weights=None, weights_method='cov', wargs=(), has_optimal_weights=True, optim_method='bfgs', optim_args=None)
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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 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 subclassesinv_weights = z?z
wherez
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, HC0currently available methods are
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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
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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 tocov
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maxlag
: int number of lags to include in HAC calculation , applies only tohac
- 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.
Returns: results : instance of GMMResults
this is also attached as attribute 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.
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