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 Dynamic, the Static, and the Weak factor models and the analysis of high-dimensional time series
M Barigozzi, M Hallin - arXiv preprint arXiv:2407.10653, 2024 - arxiv.org
Several fundamental and closely interconnected issues related to factor models are
reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak …
reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak …
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 …
Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglomerative clustering
algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of …
algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of …
Testing for sparse idiosyncratic components in factor-augmented regression models
J Beyhum, J Striaukas - Journal of Econometrics, 2024 - Elsevier
We propose a novel bootstrap test of a dense model, namely factor regression, against a
sparse plus dense alternative model augmented with sparse idiosyncratic components. The …
sparse plus dense alternative model augmented with sparse idiosyncratic components. The …
NIRVAR: Network Informed Restricted Vector Autoregression
B Martin, FS Passino, M Cucuringu, A Luati - arXiv preprint arXiv …, 2024 - arxiv.org
High-dimensional panels of time series arise in many scientific disciplines such as
neuroscience, finance, and macroeconomics. Often, co-movements within groups of the …
neuroscience, finance, and macroeconomics. Often, co-movements within groups of the …
Tail-robust factor modelling of vector and tensor time series in high dimensions
We study the problem of factor modelling vector-and tensor-valued time series in the
presence of heavy tails in the data, which produce anomalous observations with non …
presence of heavy tails in the data, which produce anomalous observations with non …
Robust Estimation in Network Vector Autoregression with Nonstationary Regressors
C Katsouris - arXiv preprint arXiv:2401.04050, 2024 - arxiv.org
This article studies identification and estimation for the network vector autoregressive model
with nonstationary regressors. In particular, network dependence is characterized by a …
with nonstationary regressors. In particular, network dependence is characterized by a …
Bayesian Inference for High-dimensional Time Series by Latent Process Modeling
Time series data arising in many applications nowadays are high-dimensional. A large
number of parameters describe features of these time series. We propose a novel approach …
number of parameters describe features of these time series. We propose a novel approach …