272 citations · 850 across the 27 of their papers we have counts for
10 papers · 1 filter
Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models
Matteo Barigozzi
This paper investigates the properties of Quasi Maximum Likelihood estimation of an approximate factor model for an -dimensional vector of stationary time series. We prove that…
Hierarchical DCC-HEAVY Model for High-Dimensional Covariance Matrices
Emilija Dzuverovic, Matteo Barigozzi
We introduce a HD DCC-HEAVY class of hierarchical-type factor models for high-dimensional covariance matrices, employing the realized measures built from higher-frequency data. The…
Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review
Matteo Barigozzi
We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series. We consider two cases: (1) estimation when no dynamic model for the facto…
Multidimensional dynamic factor models
Matteo Barigozzi, Filippo Pellegrino
This paper generalises dynamic factor models for multidimensional dependent data. In doing so, it develops an interpretable technique to study complex information sources ranging f…
Principal Component Analysis for High-Dimensional Approximate Factor Models in Time Series: Assumptions, Asymptotic Theory, and Identification
Matteo Barigozzi
We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results e…
Modelling Large Dimensional Datasets with Markov Switching Factor Models
Matteo Barigozzi, Daniele Massacci
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 r…
Factor Network Autoregressions
Matteo Barigozzi, Giuseppe Cavaliere, Graziano Moramarco
We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections b…
Inference in heavy-tailed non-stationary multivariate time series
Matteo Barigozzi, Giuseppe Cavaliere, Lorenzo Trapani
We study inference on the common stochastic trends in a non-stationary, -variate time series , in the possible presence of heavy tails. We propose a novel methodology whi…
Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models
Matteo Barigozzi, Matteo Luciani
This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the…
Generalized Dynamic Factor Models and Volatilities: Consistency, rates, and prediction intervals
Matteo Barigozzi, Marc Hallin
Volatilities, in high-dimensional panels of economic time series with a dynamic factor structure on the levels or returns, typically also admit a dynamic factor decomposition. We c…