Application of Big Data Analysis in Macroeconomic Models: A Case Study on Consumer Price Index (CPI) Forecasting

Zhentao Jiang (), Hao Zheng, Yu Yan, Xinyue Zhang and Zhe Wang
Additional contact information
Zhentao Jiang: Harbin University of Commerce, School of Economics
Hao Zheng: Harbin University of Commerce, School of Accounting
Yu Yan: Harbin University of Commerce, School of Accounting
Xinyue Zhang: Harbin University of Commerce, College of Public Finance and Administration
Zhe Wang: Harbin University of Commerce, Finance School

A chapter in Proceedings of the 2025 10th International Conference on Financial Innovation and Economic Development (ICFIED 2025), 2025, pp 485-496 from Springer

Abstract: Abstract Investigating the synergy between big data analysis and machine learning within the realm of macroeconomic modeling, the study delves into the forecasting of the consumer price index (CPI). It underscores the importance of harnessing high-dimensional data to develop machine learning models that are not only predictive but also interpretable, thereby enhancing the precision of CPI predictions. A comparative analysis of the efficacy of various machine learning models, with an emphasis on Random Forest and XGBoost for long-term forecasting, is presented. The research extends to the construction of high-frequency economic indicators from internet-based price data and the optimization of traditional economic metrics through big data insights. Theoretical derivations and empirical analyses, complemented by five pivotal visualizations, reveal the strengths and limitations of current methodologies and point the way for future investigative paths. The study concludes with the implications of these findings for the evolution of macroeconomic modeling and the formulation of economic policies, highlighting the significant role big data can play in these domains.

Keywords: Big Data Analysis; Machine Learning; Macroeconomic Models; Consumer Price Index (CPI) Forecasting; High-Dimensional Data; Model Interpretability; High-Frequency Indicators; Economic Policy Impact; Real-Time Forecasting (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-702-1_51

Ordering information: This item can be ordered from
http://www.springer.com/9789464637021

DOI: 10.2991/978-94-6463-702-1_51

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().