statsmodels.sandbox.regression.gmm.IV2SLS

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

Instrumental variables estimation using Two-Stage Least-Squares (2SLS)

Parameters

endogndarray

Endogenous variable, 1-dimensional or 2-dimensional array nobs by 1

exogndarray

Explanatory variables, 1-dimensional or 2-dimensional array nobs by k

instrumentndarray

Instruments for explanatory variables. Must contain both exog variables that are not being instrumented and instruments

Notes

All variables in exog are instrumented in the calculations. If variables in exog are not supposed to be instrumented, then these variables must 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.)

Attributes

endog_names

Names of endogenous variables.

exog_names

Names of exogenous variables.

Methods

fit()	estimate model using 2SLS IV regression
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	The Hessian matrix of the model.
information(params)	Fisher information matrix of model.
initialize()	Initialize (possibly re-initialize) a Model instance.
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)	Not implemented

Methods

fit()	estimate model using 2SLS IV regression
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
hessian(params)	The Hessian matrix of the model.
information(params)	Fisher information matrix of model.
initialize()	Initialize (possibly re-initialize) a Model instance.
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)	Not implemented

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.