---
title: Forecasting CPI
url: https://www.ml-quant.com/papers/repec/wly-jforec-v-43-y-2024-i-3-p-702-753/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:wly:jforec:v:43:y:2024:i:3:p:702-753
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffor.3048%3Bh%3Drepec%3Awly%3Ajforec%3Av%3A43%3Ay%3A2024%3Ai%3A3%3Ap%3A702-753
featured: 2024-04-03
citations: unknown
topic: Econometrics & Forecasting
---


# Forecasting CPI

The study enhances the precision and promptness of Consumer Price Index (CPI) forecasts by using a large Chinese news corpus and Internet search data, and combining penalized regression and mixed-frequency data sampling methods.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1002%2Ffor.3048%3Bh%3Drepec%3Awly%3Ajforec%3Av%3A43%3Ay%3A2024%3Ai%3A3%3Ap%3A702-753
- Identifier: RePEc:wly:jforec:v:43:y:2024:i:3:p:702-753
- Released: 2024-04-03
- First featured: Quant Letter No. 43 (2024-04-03): https://www.ml-quant.com/issues/2024-04-03/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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