class for linear instrumental variables models estimated with GMM
Uses closed form expression instead of nonlinear optimizers for each step of the iterative GMM.
The model is assumed to have the following moment condition
E( z * (y - x beta)) = 0
Where y is the dependent endogenous variable, x are the explanatory variables and z are the instruments. Variables in x that are exogenous need also be included in z.
Notation Warning: our name exog stands for the explanatory variables, and includes both exogenous and explanatory variables that are endogenous, i.e. included endogenous variables
Parameters: | endog : array_like
exog : array_like
instruments : array_like
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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 for linear model |
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() | |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
get_error(params) | |
gmmobjective(params, weights) | objective function for GMM minimization |
gmmobjective_cu(params[, weights_method, wargs]) | objective function for continuously updating GMM minimization |
gradient_momcond(params, **kwds) | |
momcond(params) | |
momcond_mean(params) | mean of moment conditions, |
predict(params[, exog]) | |
score(params, weights, **kwds) | |
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 for linear model |
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() | |
from_formula(formula, data[, subset, drop_cols]) | Create a Model from a formula and dataframe. |
get_error(params) | |
gmmobjective(params, weights) | objective function for GMM minimization |
gmmobjective_cu(params[, weights_method, wargs]) | objective function for continuously updating GMM minimization |
gradient_momcond(params, **kwds) | |
momcond(params) | |
momcond_mean(params) | mean of moment conditions, |
predict(params[, exog]) | |
score(params, weights, **kwds) | |
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 |