亚洲社会药学
亚洲社会药学

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›› 2014, Vol. 9 ›› Issue (3): 132-136.doi:

• Drug Economy • 上一篇    下一篇

Analysis of Factors Influencing per Capita Healthcare Expenses-- Based on the Empirical Analysis of Principal Component Regression Modeland Regression with ARMA Model

FU Shu-yong1, SUN Shu-jun2   

  1. 1.School of Business Administration, Shenyang Pharmaceutical University, Shenyang 110016, China;2.Ideological and Political Department, Liaoning Institute of Science and Technology, Benxi 117022, China
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2014-09-20 发布日期:2014-09-20

Analysis of Factors Influencing per Capita Healthcare Expenses-- Based on the Empirical Analysis of Principal Component Regression Modeland Regression with ARMA Model

FU Shu-yong1, SUN Shu-jun2   

  1. 1.School of Business Administration, Shenyang Pharmaceutical University, Shenyang 110016, China;2.Ideological and Political Department, Liaoning Institute of Science and Technology, Benxi 117022, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2014-09-20 Published:2014-09-20
  • Contact: FU Shu-yong

摘要： Objective To analyze the factors that influence per capita healthcare expenses in China. Methods Data stability, influential point,
linear, autoregressive model, heteroscedasticity were put into principal component regression model and regression with ARMA
model. The tests showed parameters were relatively remarkable. Model quality and the short-term forecast effect were good, too.
Results and Conclusion It can been seen that these factors such as economic level, urbanization level and aging population are the
main factors and they work equally.

关键词: principal component, regression with ARMA model, per capita healthcare expense, level of urbanization, aging population

Abstract: Objective To analyze the factors that influence per capita healthcare expenses in China. Methods Data stability, influential point,
linear, autoregressive model, heteroscedasticity were put into principal component regression model and regression with ARMA
model. The tests showed parameters were relatively remarkable. Model quality and the short-term forecast effect were good, too.
Results and Conclusion It can been seen that these factors such as economic level, urbanization level and aging population are the
main factors and they work equally.

Key words: principal component, regression with ARMA model, per capita healthcare expense, level of urbanization, aging population

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