linearmodels.system.covariance.GMMKernelCovariance¶
- class GMMKernelCovariance(x, z, eps, w, *, sigma=None, debiased=False, constraints=None, kernel='bartlett', bandwidth=None)[source]¶
Covariance estimator for IV system estimation with homoskedastic data
- Parameters
- x
List
[ndarray
] List containing the model regressors for each equation in the system
- z
List
[ndarray
] List containing the model instruments for each equation in the system
- eps
ndarray
nobs by neq array of residuals where each column corresponds an equation in the system
- w
ndarray
Weighting matrix used in estimation
- sigma
ndarray
Residual covariance used in estimation
- constraints{
None
,LinearConstraint
} Constraints used in estimation, if any
- kernel
str
Name of kernel to use. Supported kernels include:
“bartlett”, “newey-west” : Bartlett’s kernel
“parzen”, “gallant” : Parzen’s kernel
“qs”, “quadratic-spectral”, “andrews” : Quadratic spectral kernel
- bandwidth
float
Bandwidth to use for the kernel. If not provided the optimal bandwidth will be estimated.
- x
Notes
The covariance is estimated by
\[(X'ZW^{-1}Z'X)^{-1}(X'ZW^{-1}\Omega W^{-1}Z'X)(X'ZW^{-1}Z'X)^{-1}\]where \(\Omega\) is the covariance of the moment conditions.
- Attributes
bandwidth
Bandwidth used in estimation
cov
Parameter covariance
cov_config
Optional configuration information used in covariance
kernel
Kernel used in estimation
Methods
Properties
Bandwidth used in estimation
Parameter covariance
Optional configuration information used in covariance
Kernel used in estimation