6.5.5.3.5. statsmodels.sandbox.nonparametric.kernel_extras.SingleIndexModel¶

class statsmodels.sandbox.nonparametric.kernel_extras.SingleIndexModel(endog, exog, var_type)[source]¶

Single index semiparametric model y = g(X * b) + e.

Parameters:	endog: array_like The dependent variable exog: array_like The independent variable(s) var_type: str The type of variables in X: c: continuous o: ordered u: unordered

Notes

This model resembles the binary choice models. The user knows that X and b interact linearly, but g(X * b) is unknown. In the parametric binary choice models the user usually assumes some distribution of g() such as normal or logistic.

References

See chapter on semiparametric models in [1]

Attributes

b: array_like	The linear coefficients b (betas)
bw: array_like	Bandwidths

Methods

fit(): Computes the fitted values E[Y|X] = g(X * b)

and the marginal effects dY/dX.

__init__(endog, exog, var_type)[source]¶