linearmodels.iv.model.IVGMM

class linearmodels.iv.model.IVGMM(dependent: IVData | ndarray | DataArray | DataFrame | Series, exog: IVData | ndarray | DataArray | DataFrame | Series | None, endog: IVData | ndarray | DataArray | DataFrame | Series | None, instruments: IVData | ndarray | DataArray | DataFrame | Series | None, *, weights: IVData | ndarray | DataArray | DataFrame | Series | None = None, weight_type: str = 'robust', **weight_config: Any)[source]

Estimation of IV models using the generalized method of moments (GMM)

Parameters:
dependent: IVData | ndarray | DataArray | DataFrame | Series

Endogenous variables (nobs by 1)

exog: IVData | ndarray | DataArray | DataFrame | Series | None

Exogenous regressors (nobs by nexog)

endog: IVData | ndarray | DataArray | DataFrame | Series | None

Endogenous regressors (nobs by nendog)

instruments: IVData | ndarray | DataArray | DataFrame | Series | None

Instrumental variables (nobs by ninstr)

weights: IVData | ndarray | DataArray | DataFrame | Series | None = None

Observation weights used in estimation

weight_type: str = 'robust'

Name of moment condition weight function to use in the GMM estimation

**weight_config

Additional keyword arguments to pass to the moment condition weight function

Notes

Available GMM weight functions are:

  • “unadjusted”, “homoskedastic” - Assumes moment conditions are homoskedastic

  • “robust”, “heteroskedastic” - Allows for heteroskedasticity by not autocorrelation

  • “kernel” - Allows for heteroskedasticity and autocorrelation

  • “cluster” - Allows for one-way cluster dependence

The estimator is defined as

\[\hat{\beta}_{gmm}=(X'ZW^{-1}Z'X)^{-1}X'ZW^{-1}Z'Y\]

where \(W\) is a positive definite weight matrix and \(Z\) contains both the exogenous regressors and the instruments.

Todo

  • VCV: bootstrap

Methods

estimate_parameters(x, y, z, w)

param x:

Regressor matrix (nobs by nvar)

fit(*[, iter_limit, tol, initial_weight, ...])

Estimate model parameters

from_formula(formula, data, *[, weights, ...])

param formula:

Formula modified for the IV syntax described in the notes

predict(params, *[, exog, endog, data, eval_env])

Predict values for additional data

resids(params)

Compute model residuals

wresids(params)

Compute weighted model residuals

Properties

formula

Formula used to create the model

has_constant

Flag indicating the model includes a constant or equivalent

isnull

Locations of observations with missing values

notnull

Locations of observations included in estimation