linearmodels.iv.model.IVGMMCUE.estimate_parameters¶
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IVGMMCUE.estimate_parameters(starting: ndarray[tuple[int, ...], dtype[float64]], x: ndarray[tuple[int, ...], dtype[float64]], y: ndarray[tuple[int, ...], dtype[float64]], z: ndarray[tuple[int, ...], dtype[float64]], display: int =
0, opt_options: dict[str, Any] | None =None) tuple[ndarray[tuple[int, ...], dtype[float64]], int][source]¶ Estimate the parameters by minimizing the objective function.
- Parameters:¶
- starting: ndarray[tuple[int, ...], dtype[float64]]¶
Starting values for the optimization.
- x: ndarray[tuple[int, ...], dtype[float64]]¶
Regressor matrix (nobs by nvar).
- y: ndarray[tuple[int, ...], dtype[float64]]¶
Regressand matrix (nobs by 1).
- z: ndarray[tuple[int, ...], dtype[float64]]¶
Instrument matrix (nobs by ninstr).
- display: int =
0¶ Number of iterations between displaying. Set to 0 to suppress output. The default is 0.
- opt_options: dict[str, Any] | None =
None¶ Dictionary containing additional keyword arguments to pass to scipy.optimize.minimize. The default is to pass none.
- Returns:¶
params (
numpy.ndarray) – Estimated parameters (nvar by 1).iterations (
int) – Number of iterations used by the optimizer.
See also
scipy.optimize.minimizeMinimization of a scalar function.
Notes
Exposed to facilitate estimation with other data, e.g., bootstrapped samples. Performs no error checking. The bandwidth of a kernel weight matrix that is estimated from the data is the one that is estimated from the data of the model, see
j(), and not fromx,yandz.