moderndid.npiv_est#

moderndid.npiv_est(y, x, w, x_eval=None, basis='tensor', j_x_degree=3, j_x_segments=None, k_w_degree=4, k_w_segments=None, knots='uniform', deriv_index=1, deriv_order=1, check_is_fullrank=False, w_min=None, w_max=None, x_min=None, x_max=None, data_driven=False)[source]#

Core sieve TSLS estimation for the nonparametric IV model.

Computational backend for npiv that constructs B-spline bases for \(X\) and \(W\), estimates the sieve coefficient vector \(\hat{c}_J\) by two-stage least squares, and evaluates the function estimate \(\hat{h}_J\) and its derivatives at the requested points.

Parameters:

ynumpy.ndarray of shape (n,): Outcome variable.
xnumpy.ndarray of shape (n,) or (n, p_x): Endogenous regressors.
wnumpy.ndarray of shape (n,) or (n, p_w): Instrumental variables.
x_evalnumpy.ndarray of shape (m, p_x), optional: Evaluation points for \(\hat{h}\). If None, uses x.
basis{“tensor”, “additive”, “glp”}, default=”tensor”: Multivariate basis construction for \(X\).
j_x_degreeint, default=3: Degree of B-spline basis for \(X\).
j_x_segmentsint, optional: Number of segments for \(X\) basis. If None, chosen automatically.
k_w_degreeint, default=4: Degree of B-spline basis for \(W\).
k_w_segmentsint, optional: Number of segments for \(W\) basis. If None, chosen automatically.
knots{“uniform”, “quantiles”}, default=”uniform”: Knot placement method.
deriv_indexint, default=1: Which component of \(X\) to differentiate (1-based).
deriv_orderint, default=1: Order of derivative to compute.
check_is_fullrankbool, default=False: Verify full column rank of basis matrices before estimation.
w_min, w_maxfloat, optional: Override support bounds for \(W\).
x_min, x_maxfloat, optional: Override support bounds for \(X\).

Returns:

NPIVResult: Named tuple containing estimation results.