gmm.GELEstimator

Guide & worked example · Source

gmm.GELEstimator(
    m,
    rho=rho_exponential,
    min_method='L-BFGS-B',
    verbose=False,
    log=False,
)

Generalized empirical likelihood estimator for vector-valued moments.

Parameters

Name	Type	Description	Default
m	Callable[[np.ndarray, np.ndarray], np.ndarray]	Moment function returning an (n_obs, n_moments) array for data and parameter vector.	required
rho	Callable[[np.ndarray], np.ndarray]	GEL tilt function defining the criterion (ET, EL, or CUE).	rho_exponential
min_method	str	Optimization method for the outer profile problem; the inner convex problem uses a domain-preserving Newton line search.	"L-BFGS-B"
verbose	bool	If True, enables optimizer display output.	False
log	bool	If True, emits inner-objective diagnostics to the module logger.	False

Attributes

Name	Description
J_pvalue
J_stat
Sigma
est
lam_hat
m
rho
rho_double_prime
rho_prime
se

Methods

Name	Description
fit	Fit GEL estimator with proper asymptotic standard errors
summary	Summary table with test statistics

fit

gmm.GELEstimator.fit(D, startval, startval2=None)

Fit GEL estimator with proper asymptotic standard errors

summary

gmm.GELEstimator.summary(alpha=0.05)

Summary table with test statistics