Forecasting CPI with multisource data: The value of media and internet information

Using a large Chinese news corpus and Internet search data, we conduct an in‐depth out‐of‐sample forecasting study of Consumer Price Index (CPI) with the monthly macroeconomic database. For this purpose, we combine penalized regression and mixed‐frequency data sampling (MIDAS) methods to deal with t...

Full description

Saved in:
Bibliographic Details
Published in:Journal of forecasting 2024-04, Vol.43 (3), p.702-753
Main Authors: Zheng, Tingguo, Fan, Xinyue, Jin, Wei, Fang, Kuangnan
Format: Article
Language:English
Subjects:
dynamic model averaging
Forecasting
Internet
Multisource
multisource data
News
penalized regressions
Price indexes
time‐varying contribution
Uncertainty
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Using a large Chinese news corpus and Internet search data, we conduct an in‐depth out‐of‐sample forecasting study of Consumer Price Index (CPI) with the monthly macroeconomic database. For this purpose, we combine penalized regression and mixed‐frequency data sampling (MIDAS) methods to deal with the mixed‐frequency and high‐dimensional problems. Then we measure the time‐varying contributions of data from different sources and industries via dynamic model averaging. The results show that different types of data all provide enhancement in CPI forecasting, and the inclusion of media and Internet data further improves CPI forecasts' accuracy and timeliness due to their thorough coverage and fast update. In particular, the importance of alternative data is highlighted when the economy experiencing downturn or high uncertainty.
ISSN:0277-6693
1099-131X
DOI:10.1002/for.3048