Local Constant Kernel Estimation of a Partially Linear Varying Coefficient Cointegration Model

被引:0
|
作者
Wang, Luya [1 ]
Liang, Zhongwen [2 ]
Lin, Juan [3 ,4 ]
Li, Qi [5 ,6 ]
机构
[1] Univ Int Business & Econ, Sch Banking & Finance, Beijing, Peoples R China
[2] SUNY Albany, Dept Econ, Albany, NY 12222 USA
[3] Xiamen Univ, Sch Econ, Dept Finance, Xiamen, Peoples R China
[4] Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen, Peoples R China
[5] Texas A&M Univ, Dept Econ, College Stn, TX 77843 USA
[6] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China
来源
ANNALS OF ECONOMICS AND FINANCE | 2015年 / 16卷 / 02期
关键词
Varying coefficient model; Partially linear model; Nonstationary; Cointegration; INTEGRATED PROCESSES; TIME-SERIES; STATISTICAL-INFERENCE; STOCHASTIC INTEGRALS; REGRESSIONS; CONVERGENCE;
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this paper, we consider a partially linear varying coefficient cointegration model. We focus on the estimation of constant coefficients. We derive the saymptotic result for the local constant kernel estimator, which complements the results in Li, Li, Liang and Hsiao (2013) where the local polynomial estimation methods are studied. However, Li et al. (2013) impose stronger conditions to rule out the local constant estimation due to technical difficulties. We give the full treatment of the local constant method in this paper based on a novel proof. From the simulation results reported in the paper, we show that the local constant and local linear estimators perform similarly, but the local constant method requires less data. Also, in fnite sample applications the local linear estimation could suffer from the matrix singularity problem.
引用
收藏
页码:353 / 369
页数:17
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