Matteo Barigozzi, Daniele Massacci
arXiv 18 Oct 2022 · Econometrics · publishedJournal of Econometrics (2024) · 8 citations (OpenAlex)
arXiv:2210.09828 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Urga, G. and F. Wang (2024) Estimation and inference for high dimensional factor model with regime switching | 1.000 | 12 | 4 | 100% |
| 2 | Diebold, F. X. and G. D. Rudebusch (1996) Measuring business cycles: A modern perspective | 1.000 | 6 | 5 | 100% |
| 3 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 1.000 | 6 | 4 | 100% |
| 4 | Liu, X. and R. Chen (2016) Regime-switching factor models for high-dimensional time series | 1.000 | 5 | 4 | 100% |
| 5 | Massacci, D (2017) Least squares estimation of large dimensional threshold factor models self | 0.941 | 6 | 6 | 83% |
| 6 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.928 | 10 | 5 | 80% |
| 7 | Hamilton, J. D (1989) A new approach to the economic analysis of nonstationary time series and the business cycle | 0.928 | 5 | 4 | 80% |
| 8 | Barigozzi, M., H. Cho, and P. Fryzlewicz (2018) Simultaneous multiple change-point and factor analysis for high-dimensional time series self | 0.928 | 4 | 3 | 100% |
| 9 | Massacci, D (2023) Testing for regime changes in portfolios with a large number of assets: A robust approach to factor heteroskedasticity self | 0.928 | 4 | 3 | 100% |
| 10 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.928 | 4 | 3 | 100% |
Showing the top 10 of 80 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Principal Component Analysis .3cm for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification | 0.644 | 2 | 2 |
| 2 | Regime-Switching Models for Disaggregated Data | 0.405 | 1 | 1 |