A macroeconomic monitoring index construction management method based on big data analysis

DOI

10.2991/978-94-6463-276-7_8How to use a DOI?

Keywords

CPI; PPI; big data; MIDAS; mixed-frequency data

Abstract

In order to effectively forecast and monitor China’s price data, the article takes CPI and PPI price series as an example, constructs CPI and PPI high-frequency monitoring indices using commodity price big data, collects high-frequency big commodity big data from January 1, 2009 to December 27, 2019, and constructs a mixed-frequency sampling model (MIDAS). The results show that the mixed-frequency MIDAS model with big data has better dynamic forecasting effect on CPI and PPI than the traditional ADL and GARCH models, which proves that the mixed-frequency MIDAS model with big data has better monitoring effect on China’s price data.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Liangyoutong Li
PY  - 2023
DA  - 2023/10/27
TI  - A macroeconomic monitoring index construction management method based on big data analysis
BT  - Proceedings of the 2023 4th International Conference on Big Data and Social Sciences (ICBDSS 2023)
PB  - Atlantis Press
SP  - 60
EP  - 68
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-276-7_8
DO  - 10.2991/978-94-6463-276-7_8
ID  - Li2023
ER  -