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
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.
Cite this article as:
B. Xu, J. He, and Y. Yao, “The Determinants of the Textile Index: Linear or Nonlinear,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.3, pp. 335-342, 2015.
Data files:
  1. [1] P. Hall, Q. Li, and J. Racine, “Nonparametric estimation of regression functions in the presence of irrelevant regressors,” The Revies Economics and Statistics, No.89, pp. 784-789, 2007.
  2. [2] Z. F. Li, “A pricing model for commodity house based on modified regression and principal component analysis,” Statistics and Decision, No.2, pp. 31-33, 2011.
  3. [3] P. Li, “Effectiveness evaluation on 4 trillion economic stimulus plan based on ridge regression,” Statistics and Decision, No.5, pp. 103-105, 2010.
  4. [4] J. Wen, “Research on cash dividend distribution policy in listed companies based on linear regression model,” Statistics and Decision, No.2, pp. 134-136, 2011.
  5. [5] S. R. Song, L. Li, and J. Y. Han, “Research on cash dividend distribution policy in listed companies based on linear regression model,” J. of Hebei University of Science and Technology (Social Sciences), Vol.10, No.1, pp. 1-6, 2010.
  6. [6] H. Li, C. J. Liu, and L. Gao, et al., “Logistic regression analysis on influence factors of hepatitis B,” Chin Prev Med, Vol.11, No.2, pp. 144-146, 2010.
  7. [7] B. F. Chen, Y. J. Chen, and Y. F. Zou, “Logistic regression analysis of the influential factors of on-line time among senior high school students,” J. of Pub Health and Prev Med, Vol.22, No.1, pp. 53-55, 2011.
  8. [8] B. J. Ji, “An analysis of the influencing factors for grain yield in rice,” J. of Southwest Agricultural University (Natural Science), Vol.27, No.5, pp. 579-583, 2005.
  9. [9] G. G. Chen, Y. X. Li, and H. Zheng, et al., “Study of evaluation system for the real estate bubble level based on principal component analysis,” J. of Dalian University of Technology (Social Sciences), Vol.31, No.2, pp. 6-10, 2010.
  10. [10] H. Y. Wan and S. B. Li, “Empirical study on the widened differences in personal income of our country’s city dwellers based on the principal components regression analysis,” Forecasting, Vol.28, No.1, pp. 77-80, 2009.
  11. [11] Z. P. Wang, “Regional disparity in production efficiency and decomposition of productivity growth,” The J. of Quantitative & Technical Economics, No.2, pp. 33-44, 2010.
  12. [12] Y. F. Yin, “Principal component regression analysis on factors affecting the living standard of rural area of Jilin province,” Chinese Agricultural Science Bulletin, Vol.27, No.4, pp. 410-415, 2011.
  13. [13] D. J. Henderson, C. Papageorgiou, and C. F. Parmeter, “Growth empirics without parameters,” The Economic J., No.122, pp. 125–154, 2011.
  14. [14] J. Q. Fan and T. Huang, “Profile likelihood inferences on semiparametric varying-coefficient partially linear models,” Bernoulli, Vol.11, No.6, pp. 1031-1057, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024