linearmodels.iv.model.IV2SLS¶
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class linearmodels.iv.model.IV2SLS(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)[source]¶ Estimation of IV models using two-stage least squares
- 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
Notes
The 2SLS estimator is defined
\[\begin{split}\hat{\beta}_{2SLS} & =(X'Z(Z'Z)^{-1}Z'X)^{-1}X'Z(Z'Z)^{-1}Z'Y\\ & =(\hat{X}'\hat{X})^{-1}\hat{X}'Y\\ \hat{X} & =Z(Z'Z)^{-1}Z'X\end{split}\]The 2SLS estimator is a special case of a k-class estimator with \(\kappa=1\),
Todo
VCV: bootstrap
Methods
estimate_parameters(x, y, z, kappa)Parameter estimation without error checking
fit(*[, cov_type, debiased])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 used to create the model
Flag indicating the model includes a constant or equivalent
Locations of observations with missing values
Locations of observations included in estimation