An investigation of income inequality through autoregressive integrated moving average and regression analysis

John Wang, Zhi Kacie Pei, Yawei Wang, Zhaoqiong Qin

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expanded significantly in recent decades, profoundly impacting American society. This study explores the nuances of income inequality, its ramifications, and potential remedies, analyzed through the Gini Coefficient. Advanced forecasting models, including AutoRegressive Integrated Moving Average and Regression Analysis, are employed to anticipate future patterns. The research highlights the value of healthcare analytics in understanding the complexities of income inequality. The findings underscore the pressing need for effective policies to address this mounting challenge.

Original languageEnglish
Article number100287
JournalHealthcare Analytics
Volume5
DOIs
StatePublished - Jun 2024

Keywords

  • AutoRegressive integrated moving average
  • Gini index
  • Healthcare analytics
  • Income inequality
  • Nonparametric bootstrap
  • Regression analysis

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