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
- 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
- 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_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
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) andbandwidth
(a positive integer).- Return type: