Class for estimation by Generalized Method of Moments
needs to be subclassed, where the subclass defined the moment conditions momcond
Parameters: | endog : array
exog : array
instrument : array
nmoms : None or int
kwds : anything
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Returns: | *Attributes* : results : instance of GMMResults
bse : property
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Notes
The GMM class only uses the moment conditions and does not use any data directly. endog, exog, instrument and kwds in the creation of the class instance are only used to store them for access in the moment conditions. Which of this are required and how they are used depends on the moment conditions of the subclass.
Warning:
Options for various methods have not been fully implemented and are still missing in several methods.
TODO: currently onestep (maxiter=0) still produces an updated estimate of bse and cov_params.
Methods
calc_weightmatrix(moms[, weights_method, ...]) | calculate omega or the weighting matrix |
fit([start_params, maxiter, inv_weights, ...]) | Estimate parameters using GMM and return GMMResults |
fitgmm(start[, weights, optim_method, ...]) | estimate parameters using GMM |
fitgmm_cu(start[, optim_method, optim_args]) | estimate parameters using continuously updating GMM |
fititer(start[, maxiter, start_invweights, ...]) | iterative estimation with updating of optimal weighting matrix |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
gmmobjective(params, weights) | objective function for GMM minimization |
gmmobjective_cu(params[, weights_method, wargs]) | objective function for continuously updating GMM minimization |
gradient_momcond(params[, epsilon, centered]) | gradient of moment conditions |
momcond_mean(params) | mean of moment conditions, |
predict(params[, exog]) | After a model has been fit predict returns the fitted values. |
score(params, weights[, epsilon, centered]) | |
score_cu(params[, epsilon, centered]) | |
start_weights([inv]) |
Methods
calc_weightmatrix(moms[, weights_method, ...]) | calculate omega or the weighting matrix |
fit([start_params, maxiter, inv_weights, ...]) | Estimate parameters using GMM and return GMMResults |
fitgmm(start[, weights, optim_method, ...]) | estimate parameters using GMM |
fitgmm_cu(start[, optim_method, optim_args]) | estimate parameters using continuously updating GMM |
fititer(start[, maxiter, start_invweights, ...]) | iterative estimation with updating of optimal weighting matrix |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
gmmobjective(params, weights) | objective function for GMM minimization |
gmmobjective_cu(params[, weights_method, wargs]) | objective function for continuously updating GMM minimization |
gradient_momcond(params[, epsilon, centered]) | gradient of moment conditions |
momcond_mean(params) | mean of moment conditions, |
predict(params[, exog]) | After a model has been fit predict returns the fitted values. |
score(params, weights[, epsilon, centered]) | |
score_cu(params[, epsilon, centered]) | |
start_weights([inv]) |
Attributes
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
results_class | str(object=’‘) -> string |