Modelling large dimensional datasets with Markov switching factor models

Matteo Barigozzi and Daniele Massacci

Journal of Econometrics, 2025, vol. 247, issue C

Abstract: We study a novel large dimensional approximate factor model with regime changes in the loadings driven by a latent first order Markov process. By exploiting the equivalent linear representation of the model, we first recover the latent factors by means of Principal Component Analysis. We then cast the model in state–space form, and we estimate loadings and transition probabilities through an EM algorithm based on a modified version of the Baum–Lindgren–Hamilton–Kim filter and smoother that makes use of the factors previously estimated. Our approach is appealing as it provides closed form expressions for all estimators. More importantly, it does not require knowledge of the true number of factors. We derive the theoretical properties of the proposed estimation procedure, and we show their good finite sample performance through a comprehensive set of Monte Carlo experiments. The empirical usefulness of our approach is illustrated through three applications to large U.S. datasets of stock returns, macroeconomic variables, and inflation indexes.

Keywords: Regime changes; Large factor model; Markov switching; Baum–Lindgren–Hamilton–Kim filter and smoother; Principal Component Analysis (search for similar items in EconPapers)
JEL-codes: C34 C38 C55 E3 G10 (search for similar items in EconPapers)
Date: 2025
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Working Paper: Modelling Large Dimensional Datasets with Markov Switching Factor Models (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:247:y:2025:i:c:s0304407624002707

DOI: 10.1016/j.jeconom.2024.105919

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