linearmodels.asset_pricing.covariance.KernelCovariance¶

class linearmodels.asset_pricing.covariance.KernelCovariance(xe: ndarray[tuple[int, ...], dtype[float64]], *, jacobian: ndarray | None = None, inv_jacobian: ndarray | None = None, kernel: str | None = None, bandwidth: float | None = None, center: bool = True, debiased: bool = False, df: int = 0)[source]¶

Heteroskedasticity-autocorrelation (HAC) robust covariance estimator

Parameters:¶

xe: ndarray[tuple[int, ...], dtype[float64]]¶

The scores (moment) conditions.

jacobian: ndarray | None = None¶

Jacobian. One and only one of jacobian and inv_jacobian must be provided.

inv_jacobian: ndarray | None = None¶

Inverse jacobian. One and only one of jacobian and inv_jacobian must be provided.

kernel: str | None = None¶

Kernel name. See notes for available kernels. The default is “bartlett”.

bandwidth: float | None = None¶

Non-negative integer bandwidth. If None, the optimal bandwidth is estimated.

center: bool = True¶

Flag indicating to center the scores when computing the covariance.

debiased: bool = False¶

Flag indicating to use a debiased estimator.

df: int = 0¶

Degree of freedom value ot use if debiasing.

See also

linearmodels.iv.covariance.kernel_weight_bartlett, linearmodels.iv.covariance.kernel_weight_parzen, linearmodels.iv.covariance.kernel_weight_quadratic_spectral

Methods

Properties

bandwidth	Bandwidth used in estimation
config
cov	Compute parameter covariance
inv_jacobian	Inverse Jacobian
jacobian	The Jacobian
kernel	Kernel used in estimation
s	Score/moment condition covariance
square	Flag indicating if jacobian is square