sandbox.regression.gmm.IV2SLS()

statsmodels.sandbox.regression.gmm.IV2SLS

class statsmodels.sandbox.regression.gmm.IV2SLS(endog, exog, instrument=None) [source]

Class for instrumental variables estimation using Two-Stage Least-Squares

Parameters:	endog: array 1d : endogenous variable exog : array explanatory variables instruments : array instruments for explanatory variables, needs to contain those exog variables that are not instrumented out

Notes

All variables in exog are instrumented in the calculations. If variables in exog are not supposed to be instrumented out, then these variables need also to be included in the instrument array.

Degrees of freedom in the calculation of the standard errors uses df_resid = (nobs - k_vars). (This corresponds to the small option in Stata?s ivreg2.)

Methods

fit()	estimate model using 2SLS IV regression
from_formula(formula, data[, subset])	Create a Model from a formula and dataframe.
hessian(params)	The Hessian matrix of the model
information(params)	Fisher information matrix of model
initialize()
loglike(params)	Log-likelihood of model.
predict(params[, exog])	Return linear predicted values from a design matrix.
score(params)	Score vector of model.
whiten(X)

Attributes

endog_names
exog_names