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:¶
- center : bool¶
Flag indicating to center the moment conditions before computing the weighting matrix.
- use_cue : bool¶
Flag indicating to use continuously updating estimator
- steps : int¶
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
- disp : int¶
Number of iterations between printed update. 0 or negative values suppresses output
- max_iter : int¶
Maximum number of iterations when minimizing objective. Must be positive.
- cov_type : str¶
Name of covariance estimator
- debiased : bool¶
Flag indicating whether to debias the covariance estimator using a degree of freedom adjustment
- starting : array_like¶
Starting values to use in optimization. If not provided, 2SLS estimates are used.
- opt_options : dict¶
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
, andargs
can be overridden.- **cov_config :
bool
|int
|str
Additional covariance-specific options. See Notes.
- Returns:¶
Results class with parameter estimates, covariance and test statistics
- Return type:¶
Notes
The kernel covariance estimator takes the optional arguments
kernel
, one of “bartlett”, “parzen” or “qs” (quadratic spectral) andbandwidth
(a positive integer).