Oracally Efficient Two-Step Estimation of Generalized Additive Model
SFB 649 Discussion Paper 2011-016
44 Pages Posted: 9 Jan 2017
Date Written: March 14, 2011
Abstract
Generalized additive models (GAM) are multivariate nonparametric regressions for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions. Our results are for weakly dependent data and we prove oracle efficiency. The SBK techniques is both computational expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates with the asymptotic theory.
Keywords: Bandwidths, B spline, knots, link function, mixing, Nadaraya-Watson estimator
JEL Classification: C00, C14, J01, J31
Suggested Citation: Suggested Citation