---
title: Selecting Factors in Time Series
url: https://www.ml-quant.com/papers/repec/bla-jtsera-v-46-y-2025-i-1-p-113-136/
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:bla:jtsera:v:46:y:2025:i:1:p:113-136
source_url: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjtsa.12760%3Bh%3Drepec%3Abla%3Ajtsera%3Av%3A46%3Ay%3A2025%3Ai%3A1%3Ap%3A113-136
featured: 2025-01-01
citations: unknown
topic: Econometrics & Forecasting
---


# Selecting Factors in Time Series

The paper suggests a new eigenvalue ratio criterion to determine the number of factors in static approximate factor models, validating its effectiveness through a Monte Carlo study.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fdoi.org%2F10.1111%2Fjtsa.12760%3Bh%3Drepec%3Abla%3Ajtsera%3Av%3A46%3Ay%3A2025%3Ai%3A1%3Ap%3A113-136
- Identifier: RePEc:bla:jtsera:v:46:y:2025:i:1:p:113-136
- Released: 2025-01-01
- First featured: Quant Letter No. 80 (2025-01-01): https://www.ml-quant.com/issues/2025-01-01/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

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