# linearmodels.system.gmm.HeteroskedasticWeightMatrix¶

class HeteroskedasticWeightMatrix(center=False, debiased=False)[source]

Heteroskedasticity robust weight estimation

Parameters
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

Notes

The weight matrix estimator is

$\begin{split}W & = n^{-1}\sum_{i=1}^{n}g'_ig_i \\ g_i & = (z_{1i}\epsilon_{1i},z_{2i}\epsilon_{2i},\ldots,z_{ki}\epsilon_{ki})\end{split}$

where $$g_i$$ is the vector of scores across all equations for observation i. $$z_{ji}$$ is the vector of instruments for equation j and $$\epsilon_{ji}$$ is the error for equation j for observation i. This form allows for heteroskedasticity and arbitrary cross-sectional dependence between the moment conditions.

Attributes
config

Weight estimator configuration

Methods

 sigma(eps, x) Estimate residual covariance. weight_matrix(x, z, eps, *[, sigma]) Construct a GMM weight matrix for a model.

Properties

 config Weight estimator configuration