---
title: China Business Cycle Forecasting
url: https://www.ml-quant.com/papers/repec/kap-compec-v-64-y-2024-i-5-d-10-1007-s10614-024-10549-w/
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:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-024-10549-w
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-024-10549-w%3Bh%3Drepec%3Akap%3Acompec%3Av%3A64%3Ay%3A2024%3Ai%3A5%3Ad%3A10.1007_s10614-024-10549-w
featured: 2024-11-27
citations: unknown
topic: Econometrics & Forecasting
---


# China Business Cycle Forecasting

The study uses machine learning to predict China's business cycle using various indicators, with Logistic Regression being the most successful.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs10614-024-10549-w%3Bh%3Drepec%3Akap%3Acompec%3Av%3A64%3Ay%3A2024%3Ai%3A5%3Ad%3A10.1007_s10614-024-10549-w
- Identifier: RePEc:kap:compec:v:64:y:2024:i:5:d:10.1007_s10614-024-10549-w
- Released: 2024-11-27
- First featured: Quant Letter No. 76 (2024-11-27): https://www.ml-quant.com/issues/2024-11-27/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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