---
title: ML Panel Data Regressions for Heavy-Tailed Data
url: https://www.ml-quant.com/papers/repec/eee-econom-v-237-y-2023-i-2-s0304407622001282/
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:econom:v:237:y:2023:i:2:s0304407622001282
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304407622001282%3Bh%3Drepec%3Aeee%3Aeconom%3Av%3A237%3Ay%3A2023%3Ai%3A2%3As0304407622001282
featured: 2023-12-13
citations: unknown
topic: Econometrics & Forecasting
---


# ML Panel Data Regressions for Heavy-Tailed Data

A study presents structured machine learning regressions for heavy-tailed dependent panel data, using a new concentration inequality for such data.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0304407622001282%3Bh%3Drepec%3Aeee%3Aeconom%3Av%3A237%3Ay%3A2023%3Ai%3A2%3As0304407622001282
- Identifier: RePEc:eee:econom:v:237:y:2023:i:2:s0304407622001282
- Released: 2023-12-13
- First featured: Quant Letter No. 29 (2023-12-13): https://www.ml-quant.com/issues/2023-12-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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