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

Abstract
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.
Authors
Tingguo Zheng Xinyue Fan Wei Jin and Kuangnan Fang
Keywords
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0.82
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Alternative Data
Series
Journal of Forecasting 2024 vol. 43 issue 3 702-753
Time Added
2024/03/11 03:46
Total Downloads
0
Year Published
2024
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