gmm.gmm.GMMEstimatorTorch
Guide & worked example · Source
gmm.gmm.GMMEstimatorTorch(
moment_cond,
weighting_matrix='optimal',
backend='scipy',
device=None,
)Attributes
| Name | Description |
|---|---|
W_ |
|
device |
|
moment_cond |
|
std_errors_ |
|
theta_ |
|
weighting_matrix |
Methods
| Name | Description |
|---|---|
| fit | |
| gmm_objective | |
| iv_moment | |
| jacobian_moment_cond | |
| optimal_weighting_matrix | |
| summary | |
| to |
fit
gmm.gmm.GMMEstimatorTorch.fit(
z,
y,
x,
verbose=False,
fit_method=None,
iid=True,
two_step=True,
*,
init_params=None,
max_lags=None,
tol=1e-09,
maxiter=2000,
)gmm_objective
gmm.gmm.GMMEstimatorTorch.gmm_objective(beta)iv_moment
gmm.gmm.GMMEstimatorTorch.iv_moment(z, y, x, beta)jacobian_moment_cond
gmm.gmm.GMMEstimatorTorch.jacobian_moment_cond()optimal_weighting_matrix
gmm.gmm.GMMEstimatorTorch.optimal_weighting_matrix(moments)summary
gmm.gmm.GMMEstimatorTorch.summary(prec=4, alpha=0.05)to
gmm.gmm.GMMEstimatorTorch.to(device)