Oracally Efficient Two-Step Estimation of Generalized Additive Model

SFB 649 Discussion Paper 2011-016

44 Pages Posted: 9 Jan 2017

See all articles by Rong Liu

Rong Liu

University of Toledo

Lijian Yang

Soochow University

Wolfgang Karl Härdle

Blockchain Research Center Humboldt-Universität zu Berlin; Charles University; National Yang Ming Chiao Tung University; Asian Competitiveness Institute

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

Liu, Rong and Yang, Lijian and Härdle, Wolfgang Karl, Oracally Efficient Two-Step Estimation of Generalized Additive Model (March 14, 2011). SFB 649 Discussion Paper 2011-016, Available at SSRN: https://ssrn.com/abstract=2894250 or http://dx.doi.org/10.2139/ssrn.2894250

Rong Liu

University of Toledo

Mail Stop 119, HH 3000
Toledo, OH 43606
United States

Lijian Yang

Soochow University ( email )

No. 1 Shizi Street
Taipei, Jiangsu 215006
Taiwan

Wolfgang Karl Härdle (Contact Author)

Blockchain Research Center Humboldt-Universität zu Berlin ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Asian Competitiveness Institute ( email )

Singapore

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