Statistical Inference for Generalized Additive Partially Linear Model

Journal of Multivariate Analysis, Volume 162, November 2017, Pages 1-15

47 Pages Posted: 17 Nov 2020

See all articles by Rong Liu

Rong Liu

University of Toledo

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Guoiy Zhang

affiliation not provided to SSRN

Date Written: 2017

Abstract

The class of Generalized Additive Models (GAMs) is a powerful tool which has been well studied. It helps to identify additive regression structure that can be determined even more sharply via test procedures when some component functions have a parametric form. Generalized Additive Partially Linear Models (GAPLMs) enjoy the simplicity of GLMs and the flexibility of GAMs because they combine both parametric and non-parametric components. We use the hybrid spline-back-fitted kernel estimation method, which combines the best features of both spline and kernel methods, to make fast, efficient and reliable estimation under an -mixing condition. In addition, simultaneous confidence corridors (SCCs) for testing overall trends and empirical likelihood confidence regions for parameters are provided under an independence condition. The asymptotic properties are obtained and simulation results support the theoretical properties. As an illustration, we use GAPLM methodology to improve the accuracy ratio of the default predictions for 19,610 German companies. The quantlet for this paper are available on https://github.com.

Keywords: B-Spline, Empirical Likelihood, Kernel Estimator, Link Function, Mixing

JEL Classification: C14, G33

Suggested Citation

Liu, Rong and Härdle, Wolfgang K. and Zhang, Guoiy, Statistical Inference for Generalized Additive Partially Linear Model (2017). Journal of Multivariate Analysis, Volume 162, November 2017, Pages 1-15, Available at SSRN: https://ssrn.com/abstract=3702177

Rong Liu (Contact Author)

University of Toledo

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

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

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

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Guoiy Zhang

affiliation not provided to SSRN

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