Instrumental variables estimation using Two-Stage Least-Squares (2SLS)
Parameters: | endog: array :
exog : array
instruments : array
|
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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.)
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() | |
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) |
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() | |
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 | Names of endogenous variables |
exog_names | Names of exogenous variables |