Publications (40)
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…
An algebraic estimator for large spectral density matrices
Matteo Barigozzi, Matteo Farnè
We propose a new estimator of high-dimensional spectral density matrices, called UNshrunk ALgebraic Spectral Estimator (UNALSE), under the assumption of an underlying low rank plus…
Robust Tensor Factor Analysis
Matteo Barigozzi, Yong He, Lingxiao Li +1
We consider (robust) inference in the context of a factor model for tensor-valued sequences. We study the consistency of the estimated common factors and loadings space when using…
Spatio-Temporal Patterns of the International Merger and Acquisition Network
Marco Dueñas, Rossana Mastrandrea, Matteo Barigozzi +1
This paper analyses the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-direc…
The Canonical Decomposition of Factor Models: Weak Factors are Everywhere
Philipp Gersing, Matteo Barigozzi, Christoph Rust +1
We derive a novel canonical decomposition of factor models encompassing both the static factor model - where factors are loaded only contemporaneously - and the Generalised Dynamic…
Sequential testing for structural stability in approximate factor models
Matteo Barigozzi, Lorenzo Trapani
We develop a monitoring procedure to detect changes in a large approximate factor model. Letting be the number of common factors, we base our statistics on the fact that the $\…
Determining the dimension of factor structures in non-stationary large datasets
Matteo Barigozzi, Lorenzo Trapani
We propose a procedure to determine the dimension of the common factor space in a large, possibly non-stationary, dataset. Our procedure is designed to determine whether there are…
Multinetwork of international trade: A commodity-specific analysis
Matteo Barigozzi, Giorgio Fagiolo, Diego Garlaschelli
We study the topological properties of the multinetwork of commodity-specific trade relations among world countries over the 1992-2003 period, comparing them with those of the aggr…
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…
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…
Mean Square Errors of factors extracted using principal components, linear projections, and Kalman filter
Matteo Barigozzi, Diego Fresoli, Esther Ruiz
Factor extraction from systems of variables with a large cross-sectional dimension, , is often based on either Principal Components (PC)-based procedures, or Kalman filter (KF)-…
Measuring the Euro Area Output Gap
Matteo Barigozzi, Claudio Lissona, Matteo Luciani
We measure the Euro Area (EA) output gap and potential output using a non-stationary dynamic factor model estimated on a large dataset of macroeconomic and financial variables. Our…
Tail-robust factor modelling of vector and tensor time series in high dimensions
Matteo Barigozzi, Haeran Cho, Hyeyoung Maeng
We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, which produce extreme observations with non-negligible pr…
Consistent estimation of high-dimensional factor models when the factor number is over-estimated
Matteo Barigozzi, Haeran Cho
A high-dimensional -factor model for an -dimensional vector time series is characterised by the presence of a large eigengap (increasing with ) between the -th and the…
Statistical inference for large-dimensional tensor factor model by iterative projections
Matteo Barigozzi, Yong He, Lingxiao Li +1
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order large-dimensional tensor time series, and have wide applications in economics, finance and medical…
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…
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…
On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters
Marco Capasso, Lucia Alessi, Matteo Barigozzi +1
This paper discusses some problems possibly arising when approximating via Monte-Carlo simulations the distributions of goodness-of-fit test statistics based on the empirical distr…
fnets: An R Package for Network Estimation and Forecasting via Factor-Adjusted VAR Modelling
Dom Owens, Haeran Cho, Matteo Barigozzi
The package fnets for the R language implements the suite of methodologies proposed by Barigozzi et al. (2022) for the network estimation and forecasting of high-dimensional time s…
Identifying the Community Structure of the International-Trade Multi Network
Matteo Barigozzi, Giorgio Fagiolo, Giuseppe Mangioni
We study the community structure of the multi-network of commodity-specific trade relations among world countries over the 1992-2003 period. We compare structures across commoditie…
Estimation of large approximate dynamic matrix factor models based on the EM algorithm and Kalman filtering
Matteo Barigozzi, Luca Trapin
This paper considers an approximate dynamic matrix factor model that accounts for the time series nature of the data by explicitly modelling the time evolution of the factors. We s…
Simultaneous multiple change-point and factor analysis for high-dimensional time series
Matteo Barigozzi, Haeran Cho, Piotr Fryzlewicz
We propose the first comprehensive treatment of high-dimensional time series factor models with multiple change-points in their second-order structure. We operate under the most fl…
Large-Dimensional Dynamic Factor Models: Estimation of Impulse-Response Functions with Cointegrated Factors
Matteo Barigozzi, Marco Lippi, Matteo Luciani
We study a large-dimensional Dynamic Factor Model where: (i)~the vector of factors is and driven by a number of shocks that is smaller than the dimension of $\…
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…
Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series
Matteo Barigozzi, Marc Hallin
We consider weighted directed networks for analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\&P100 inde…
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…
Dynamic Factor Models: a Genealogy
Matteo Barigozzi, Marc Hallin
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 infe…
Maximum entropy approaches for the study of triadic motifs in the Mergers & Acquisitions network
Ihusan Adam, Stefano Garlaschi, Jian-Hong Lin +4
In the past years statistical physics has been successfully applied for complex networks modelling. In particular, it has been shown that the maximum entropy principle can be explo…
Dynamic Factor Models, Cointegration, and Error Correction Mechanisms
Matteo Barigozzi, Marco Lippi, Matteo Luciani
The paper studies Non-Stationary Dynamic Factor Models such that the factors are and singular, i.e. has dimension and is driven by a -dime…
The Dynamic, the Static, and the Weak: Factor models and the analysis of high-dimensional time series
Matteo Barigozzi, Marc Hallin
Several fundamental and closely interconnected issues related to factor models are reviewed and discussed: dynamic versus static loadings, rate-strong versus rate-weak factors, the…
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…
Common factors, trends, and cycles in large datasets
Matteo Barigozzi, Matteo Luciani
This paper considers a non-stationary dynamic factor model for large datasets to disentangle long-run from short-run co-movements. We first propose a new Quasi Maximum Likelihood e…
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…
Predicting Energy Demand with Tensor Factor Models
Mattia Banin, Matteo Barigozzi, Luca Trapin
Hourly consumption from multiple providers displays pronounced intra-day, intra-week, and annual seasonalities, as well as strong cross-sectional correlations. We introduce a novel…
General Spatio-Temporal Factor Models for High-Dimensional Random Fields on a Lattice
Matteo Barigozzi, Davide La Vecchia, Hang Liu
Motivated by the need for analysing large spatio-temporal panel data, we introduce a novel dimensionality reduction methodology for -dimensional random fields observed across a…
Large datasets for the Euro Area and its member countries and the dynamic effects of the common monetary policy
Matteo Barigozzi, Claudio Lissona, Lorenzo Tonni
We introduce EA-MD-QD, a new publicly available dataset comprising 1136 macroeconomic time series for the euro area (EA) and its ten largest member countries observed at monthly or…
Moving sum procedure for multiple change point detection in large factor models
Matteo Barigozzi, Haeran Cho, Lorenzo Trapani
This paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loa…
FNETS: Factor-adjusted network estimation and forecasting for high-dimensional time series
Matteo Barigozzi, Haeran Cho, Dom Owens
We propose FNETS, a methodology for network estimation and forecasting of high-dimensional time series exhibiting strong serial- and cross-sectional correlations. We operate under…
Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm
Matteo Barigozzi, Matteo Luciani
We study estimation of large Dynamic Factor models implemented through the Expectation Maximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both the cros…
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…