6.5.5.3.4. statsmodels.sandbox.nonparametric.kernel_extras.SemiLinear¶
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class
statsmodels.sandbox.nonparametric.kernel_extras.SemiLinear(endog, exog, exog_nonparametric, var_type, k_linear)[source]¶ Semiparametric partially linear model,
Y = Xb + g(Z) + e.Parameters: endog: array_like
The dependent variable
exog: array_like
The linear component in the regression
exog_nonparametric: array_like
The nonparametric component in the regression
var_type: str
The type of the variables in the nonparametric component;
- c: continuous
- o: ordered
- u: unordered
k_linear : int
The number of variables that comprise the linear component.
Notes
This model uses only the local constant regression estimator
References
See chapter on Semiparametric Models in [1]
Attributes
bw: array_like Bandwidths for the nonparametric component exog_nonparametric b: array_like Coefficients in the linear component nobs (int) The number of observations. k_linear (int) The number of variables that comprise the linear component. Methods
fit(): Returns the fitted mean and marginal effects dy/dz
6.5.5.3.4.1. Methods¶
__init__(endog, exog, exog_nonparametric, ...) |
|
aic_hurvich(bw[, func]) |
Computes the AIC Hurvich criteria for the estimation of the bandwidth. |
cv_loo(params) |
Similar to the cross validation leave-one-out estimator. |
fit([exog_predict, exog_nonparametric_predict]) |
Computes fitted values and marginal effects |
loo_likelihood() |
|
r_squared() |
Returns the R-Squared for the nonparametric regression. |
sig_test(var_pos[, nboot, nested_res, pivot]) |
Significance test for the variables in the regression. |