---
title: Economic Growth Forecasting in Sverdlovsk Region
url: https://www.ml-quant.com/papers/repec/aiy-jnjaer-v-23-y-2024-i-3-p-674-695/
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:aiy:jnjaer:v:23:y:2024:i:3:p:674-695
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fjournalaer.ru%2F%2Ffileadmin%2Fuser_upload%2Fsite_15934%2F2024%2F05_Balungu_Kumar.pdf%3Bh%3Drepec%3Aaiy%3Ajnjaer%3Av%3A23%3Ay%3A2024%3Ai%3A3%3Ap%3A674-695
featured: 2024-10-17
citations: unknown
topic: Econometrics & Forecasting
---


# Economic Growth Forecasting in Sverdlovsk Region

Machine learning, particularly the random forest model, is more effective in predicting the Gross Regional Product growth of Russia's Sverdlovsk region than traditional models, according to a study.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fjournalaer.ru%2F%2Ffileadmin%2Fuser_upload%2Fsite_15934%2F2024%2F05_Balungu_Kumar.pdf%3Bh%3Drepec%3Aaiy%3Ajnjaer%3Av%3A23%3Ay%3A2024%3Ai%3A3%3Ap%3A674-695
- Identifier: RePEc:aiy:jnjaer:v:23:y:2024:i:3:p:674-695
- Released: 2024-10-17
- First featured: Quant Letter No. 70 (2024-10-17): https://www.ml-quant.com/issues/2024-10-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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