Dynamic factor models: A genealogy
M Barigozzi, M Hallin - Partial Identification in Econometrics and Related …, 2024 - Springer
Dynamic factor models have been developed out of the need of analyzing and forecasting
time series in increasingly high dimensions. While mathematical statisticians faced with …
time series in increasingly high dimensions. While mathematical statisticians faced with …
High-dimensional time series segmentation via factor-adjusted vector autoregressive modeling
Vector autoregressive (VAR) models are popularly adopted for modeling high-dimensional
time series, and their piecewise extensions allow for structural changes in the data. In VAR …
time series, and their piecewise extensions allow for structural changes in the data. In VAR …
The likelihood ratio test for structural changes in factor models
A factor model with a break in its factor loadings is observationally equivalent to a model
without changes in the loadings but with a change in the variance of its factors. This …
without changes in the loadings but with a change in the variance of its factors. This …
xtnumfac: A battery of estimators for the number of common factors in time series and panel-data models
In this article, we introduce a new community-contributed command, xtnumfac, for estimating
the number of common factors in time-series and panel datasets using the methods of Bai …
the number of common factors in time-series and panel datasets using the methods of Bai …
Online change-point detection for matrix-valued time series with latent two-way factor structure
Online change-point detection for matrix-valued time series with latent two-way factor structure
Page 1 The Annals of Statistics 2024, Vol. 52, No. 4, 1646–1670 https://doi.org/10.1214/24-AOS2410 …
Page 1 The Annals of Statistics 2024, Vol. 52, No. 4, 1646–1670 https://doi.org/10.1214/24-AOS2410 …
[HTML][HTML] Estimating time-varying networks for high-dimensional time series
We explore time-varying networks for high-dimensional locally stationary time series, using
the large VAR model framework with both the transition and (error) precision matrices …
the large VAR model framework with both the transition and (error) precision matrices …
On estimation and inference of large approximate dynamic factor models via the principal component analysis
M Barigozzi - arXiv preprint arXiv:2211.01921, 2022 - arxiv.org
We provide an alternative derivation of the asymptotic results for the Principal Components
estimator of a large approximate factor model. Results are derived under a minimal set of …
estimator of a large approximate factor model. Results are derived under a minimal set of …
Modelling large dimensional datasets with markov switching factor models
M Barigozzi, D Massacci - Journal of Econometrics, 2025 - Elsevier
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 …
loadings driven by a latent first order Markov process. By exploiting the equivalent linear …
Reprint of: The likelihood ratio test for structural changes in factor models
A factor model with a break in its factor loadings is observationally equivalent to a model
without changes in the loadings but with a change in the variance of its factors. This …
without changes in the loadings but with a change in the variance of its factors. This …
Underlying core inflation with multiple regimes
G Rodriguez-Rondon - arXiv preprint arXiv:2411.12845, 2024 - arxiv.org
This paper introduces a new approach for estimating core inflation indicators based on
common factors across a broad range of price indices. Specifically, by utilizing procedures …
common factors across a broad range of price indices. Specifically, by utilizing procedures …