Selecting the number of factors in multi‐variate time series

Angela Caro and Daniel Peña

Journal of Time Series Analysis, 2025, vol. 46, issue 1, 113-136

Abstract: How many factors are there? It is a critical question that researchers and practitioners deal with when estimating factor models. We proposed a new eigenvalue ratio criterion for the number of factors in static approximate factor models. It considers a pooled squared correlation matrix which is defined as a weighted combination of the main observed squared correlation matrices. Theoretical results are given to justify the expected good properties of the criterion, and a Monte Carlo study shows its good finite sample performance in different scenarios, depending on the idiosyncratic error structure and factor strength. We conclude comparing different criteria in a forecasting exercise with macroeconomic data.

Date: 2025
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https://doi.org/10.1111/jtsa.12760

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jtsera:v:46:y:2025:i:1:p:113-136

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