linearmodels.asset_pricing.model.LinearFactorModelGMM.fit

LinearFactorModelGMM.fit(*, center=True, use_cue=False, steps=2, disp=10, max_iter=1000, cov_type='robust', debiased=True, starting=None, opt_options=None, **cov_config)[source]

Estimate model parameters

Parameters:
centerbool

Flag indicating to center the moment conditions before computing the weighting matrix.

use_cuebool

Flag indicating to use continuously updating estimator

stepsint

Number of steps to use when estimating parameters. 2 corresponds to the standard efficient GMM estimator. Higher values will iterate until convergence or up to the number of steps given

dispint

Number of iterations between printed update. 0 or negative values suppresses output

max_iterint

Maximum number of iterations when minimizing objective. Must be positive.

cov_typestr

Name of covariance estimator

debiasedbool

Flag indicating whether to debias the covariance estimator using a degree of freedom adjustment

startingarray_like

Starting values to use in optimization. If not provided, 2SLS estimates are used.

opt_optionsdict

Additional options to pass to scipy.optimize.minimize when optimizing the objective function. If not provided, defers to scipy to choose an appropriate optimizer. All minimize inputs except fun, x0, and args can be overridden.

**cov_config

Additional covariance-specific options. See Notes.

Returns:
GMMFactorModelResults

Results class with parameter estimates, covariance and test statistics

Notes

The kernel covariance estimator takes the optional arguments kernel, one of “bartlett”, “parzen” or “qs” (quadratic spectral) and bandwidth (a positive integer).

Return type:

GMMFactorModelResults