Nonparametric Time-Varying Coefficient Models for Panel Data

Huazhen Lin (), Hyokyoung G. Hong, Baoying Yang, Wei Liu, Yong Zhang, Gang-Zhi Fan and Yi Li ()
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Huazhen Lin: Southwestern University of Finance and Economics
Hyokyoung G. Hong: Michigan State University
Baoying Yang: Southwest Jiaotong University
Wei Liu: Southwestern University of Finance and Economics
Yong Zhang: Southwestern University of Finance and Economics
Gang-Zhi Fan: Konkuk University
Yi Li: University of Michigan

Statistics in Biosciences, 2019, vol. 11, issue 3, No 4, 548-566

Abstract: Abstract The collection rate of contributions to public pension (CRCP), expressed as the ratio of the actual contributions to the expected contributions from insurers, is a key component of the public pension system in China. Recent years have seen various patterns of change in CRCPs at the provincial level. In order to study the drastic changes in a short time and understand their underlying implications, we propose a nonparametric time-varying coefficients model for longitudinal data with pre-specified finite time points, also known as panel data. By utilizing a penalized least squares method, the proposed method enables estimation of a large number of parameters, which can exceed the sample size. The resulting estimator is shown to be efficient, robust, and computationally feasible. Furthermore, it possesses desirable theoretical properties such as $$n^{1/2}$$ n 1 / 2 -consistency, asymptotic normality, and the oracle property.

Keywords: Collection rate of public pension contributions; Nonparametric time-varying coefficients model; Panel data; Penalized least squares estimation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s12561-019-09248-0

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