---
title: ML Forecasting for Standard Dominance
url: https://www.ml-quant.com/papers/repec/eee-tefoso-v-205-y-2024-i-c-s0040162524002956/
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:eee:tefoso:v:205:y:2024:i:c:s0040162524002956
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0040162524002956%3Bh%3Drepec%3Aeee%3Atefoso%3Av%3A205%3Ay%3A2024%3Ai%3Ac%3As0040162524002956
featured: 2024-07-10
citations: unknown
topic: Econometrics & Forecasting
---


# ML Forecasting for Standard Dominance

The study uses machine learning to predict the results of standard battles in the Chinese solid-state lighting industry, indicating that strong alliances, patent application experience, and marketization level increase a firm's chances of winning.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0040162524002956%3Bh%3Drepec%3Aeee%3Atefoso%3Av%3A205%3Ay%3A2024%3Ai%3Ac%3As0040162524002956
- Identifier: RePEc:eee:tefoso:v:205:y:2024:i:c:s0040162524002956
- Released: 2024-07-10
- First featured: Quant Letter No. 56 (2024-07-10): https://www.ml-quant.com/issues/2024-07-10/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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