6.7.4.4.5. statsmodels.sandbox.regression.gmm.IVGMM¶
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class
statsmodels.sandbox.regression.gmm.
IVGMM
(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)[source]¶ Basic class for instrumental variables estimation using GMM
A linear function for the conditional mean is defined as default but the methods should be overwritten by subclasses, currently LinearIVGMM and NonlinearIVGMM are implemented as subclasses.
See also
maybe drop and use mixin instead
TODO: GMM doesn’t really care about the data, just the moment conditions
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__init__
(endog, exog, instrument, k_moms=None, k_params=None, missing='none', **kwds)¶ maybe drop and use mixin instead
TODO: GMM doesn’t really care about the data, just the moment conditions
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6.7.4.4.5.1. Methods¶
__init__ (endog, exog, instrument[, k_moms, ...]) |
maybe drop and use mixin instead |
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 |
fitstart () |
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from_formula (formula, data[, subset]) |
Create a Model from a formula and dataframe. |
get_error (params) |
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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 (params) |
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momcond_mean (params) |
mean of moment conditions, |
predict (params[, exog]) |
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score (params, weights[, epsilon, centered]) |
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score_cu (params[, epsilon, centered]) |
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start_weights ([inv]) |
6.7.4.4.5.2. Attributes¶
endog_names |
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exog_names |
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results_class |