kernel_regression: kernel regression estimator usage: fit = kernel_regression(eval_points, depvar, condvars, bandwidth) inputs: eval_points: PxK matrix of points at which to calculate the density depvar: Nx1 vector of observations of the dependent variable condvars: NxK matrix of data points bandwidth (optional): positive scalar, the smoothing parameter. Default is N ^ (-1/(4+K)) kernel (optional): string. Name of the kernel function. Default is Gaussian kernel. prewhiten bool (optional): default true. If true, rotate data using Choleski decomposition of inverse of covariance, to approximate independence after the transformation, which makes a product kernel a reasonable choice. do_cv: bool (optional). default false. If true, calculate leave-1-out fit to calculate the cross validation score computenodes: int (optional, default 0). Number of compute nodes for parallel evaluation debug: bool (optional, default false). show results on compute nodes if doing a parallel run outputs: fit: Px1 vector: the fitted value at each of the P evaluation points.
Package: econometrics