linearmodels.iv.gmm.KernelWeightMatrix

class KernelWeightMatrix(kernel='bartlett', bandwidth=None, center=False, debiased=False, optimal_bw=False)[source]

Heteroskedasticity, autocorrelation robust weight estimation

Parameters
kernelstr

Name of kernel weighting function to use

bandwidth{int, None}

Bandwidth to use when computing kernel weights

centerbool

Flag indicating whether to center the moment conditions by subtracting the mean before computing the weight matrix.

debiasedbool

Flag indicating whether to use small-sample adjustments

optimal_bwbool

Flag indicating whether to estimate the optimal bandwidth, when bandwidth is None. If False, nobs - 2 is used

Notes

Supported kernels:

  • “bartlett”, “newey-west” - Bartlett’s kernel

  • “parzen”, “gallant” - Parzen’s kernel

  • “qs”, “quadratic-spectral”, “andrews” - The quadratic spectral kernel

\[\begin{split}g_i & =z_i \epsilon_i \\ W & =n^{-1}(\Gamma_0+\sum_{j=1}^{n-1}k(j)(\Gamma_j+\Gamma_j')) \\ \Gamma_j & =\sum_{i=j+1}^n g'_i g_{j-j}\end{split}\]

where \(k(j)\) is the kernel weight for lag j and \(z_i\) contains both the exogenous regressors and instruments..

Attributes
bandwidth

Actual bandwidth used in estimating the weight matrix

config

Weight estimator configuration

Methods

weight_matrix(x, z, eps)

Parameters

Properties

bandwidth

Actual bandwidth used in estimating the weight matrix

config

Weight estimator configuration