linearmodels.asset_pricing.model.TradedFactorModel.fit¶

TradedFactorModel.fit(cov_type: str = 'robust', debiased: bool = True, **cov_config: str | float) → LinearFactorModelResults[source]¶

Estimate model parameters

Parameters:¶

cov_type: str = 'robust'¶

Name of covariance estimator

debiased: bool = True¶

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

**cov_config : dict

Additional covariance-specific options. See Notes.

Returns:¶

Results class with parameter estimates, covariance and test statistics

Return type:¶

linearmodels.asset_pricing.results.LinearFactorModelResults

Notes

Supported covariance estimators are:

• “robust” - Heteroskedasticity-robust covariance estimator

• “kernel” - Heteroskedasticity and Autocorrelation consistent (HAC) covariance estimator

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