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Selecting the number of factors in multi‐variate time series

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  • Angela Caro
  • Daniel Peña

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.

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  • Angela Caro & Daniel Peña, 2025. "Selecting the number of factors in multi‐variate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 46(1), pages 113-136, January.
    • Handle: RePEc:bla:jtsera:v:46:y:2025:i:1:p:113-136
    DOI: 10.1111/jtsa.12760
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