JACIII Vol.19 No.3 pp. 335-342
doi: 10.20965/jaciii.2015.p0335


The Determinants of the Textile Index: Linear or Nonlinear

Bing Xu*, Jinghui He*,**, and Yanyun Yao*,**

*Research Institute of Econometrics and Statistics, Zhejiang Gongshang University
18 Xuezheng Road, Xiasha University Town, Hangzhou 310018, China
**School of Mathematics, Physics and Information Science, Shaoxing College of Arts and Sciences
508 West Huancheng Road, Shaoxing 312000, China

December 15, 2013
February 26, 2015
Online released:
May 20, 2015
May 20, 2015
local-constant least squares, local-linear least squares, base model, path model, semiparametric time-varying coefficient model

This paper analyzes the Keqiao Textile Index of China, which reflects China Textile City, the leading wholesale textile market in China. China Textile City is a textile entrepot with the most extensive scale and the largest line of business in China, and it is the largest specialized market for light textile in Asia as well. Thus, it is worthwhile to analyze this index. In this paper, 10 variables that represent the factors that significantly influence the Textile Index are selected from the set of possible variables that are deemed to be valid to the index. 6 variables are identified as nonlinear and 4 as linear by the nonparametric method. Then, varying-coefficient partially linear models are established, dividing the index into five terms: one nonparametric term and four linear terms. Each of the five terms comprises approximately 20% of the index, with the linear terms accounting for nearly 80%. Among the six nonparametric variables, cotton index A plays the most important role. The empirical and simulated results consistently show that the percent of each of the five terms would not vary substantially during the sample period if cotton index A were not more than twice the sample mean. Thus, textile prices can be regulated by properly adjusting the cotton price.

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Last updated on Mar. 28, 2017