5 citations · 12 across the 19 of their papers we have counts for
23 papers
A Bayesian Dynamic Latent Space Model for Weighted Networks
Roberto Casarin, Matteo Iacopini, Antonio Peruzzi
A new dynamic latent space eigenmodel (LSM) is proposed for weighted temporal networks. The model accommodates integer-valued weights, excess of zeros, time-varying node positions…
Bayesian Markov-Switching Partial Reduced-Rank Regression
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-Rank (RR) regression is a powerful dimensionality reduction technique but it overlooks any possible group configuration among the responses by assuming a low-rank structure…
Bayesian Partial Reduced-Rank Regression
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-rank (RR) regression may be interpreted as a dimensionality reduction technique able to reveal complex relationships among the data parsimoniously. However, RR regression m…
A Quantile Nelson-Siegel model
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
We propose a novel framework for modeling the yield curve from a quantile perspective. Building on the dynamic Nelson-Siegel model of Diebold et al. (2006), we extend its tradition…
A Bayesian Markov-switching SAR model for time-varying cross-price spillovers
Christian Glocker, Matteo Iacopini, Tamás Krisztin +1
The spatial autoregressive (SAR) model is extended by introducing a Markov switching dynamics for the weight matrix and spatial autoregressive parameter. The framework enables the…
Bayesian SAR model with stochastic volatility and multiple time-varying weights
Michele Costola, Matteo Iacopini, Casper Wichers
A novel spatial autoregressive model for panel data is introduced, which incorporates multilayer networks and accounts for time-varying relationships. Moreover, the proposed approa…
Static and Dynamic BART for Rank-Order Data
Matteo Iacopini, Eoghan O'Neill, Luca Rossini
Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order dat…
A Spatiotemporal Gamma Shot Noise Cox Process
Federico Bassetti, Roberto Casarin, Matteo Iacopini +1
A new discrete-time shot noise Cox process for spatiotemporal data is proposed. The random intensity is driven by a dependent sequence of latent gamma random measures. Some propert…
Money Growth and Inflation: A Quantile Sensitivity Approach
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
An innovative method is proposed to construct a quantile dependence system for inflation and money growth. By considering all quantiles and leveraging a novel notion of quantile se…
Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-rank regression recognises the possibility of a rank-deficient matrix of coefficients. We propose a novel Bayesian model for estimating the rank of the coefficient matrix,…
Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This article proposes a novel Bayesian multivariate quantile regression to forecast the tail behavior of energy commodities, where the homoskedasticity assumption is relaxed to all…
Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP
Matteo Iacopini, Aubrey Poon, Luca Rossini +1
Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content…
On the "mementum" of Meme Stocks
Michele Costola, Matteo Iacopini, Carlo R. M. A. Santagiustina
The meme stock phenomenon is yet to be explored. In this note, we provide evidence that these stocks display common stylized facts on the dynamics of price, trading volume, and soc…
COVID-19 spreading in financial networks: A semiparametric matrix regression model
Billio Monica, Casarin Roberto, Costola Michele +1
Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametri…
Filtering the intensity of public concern from social media count data with jumps
Matteo Iacopini, Carlo R. M. A. Santagiustina
Count time series obtained from online social media data, such as Twitter, have drawn increasing interest among academics and market analysts over the past decade. Transforming Web…
Visualizing and comparing distributions with half-disk density strips
Carlo Romano Marcello Alessandro Santagiustina, Matteo Iacopini
We propose a user-friendly graphical tool, the half-disk density strip (HDDS), for visualizing and comparing probability density functions. The HDDS exploits color shading for repr…
Proper scoring rules for evaluating asymmetry in density forecasting
Matteo Iacopini, Francesco Ravazzolo, Luca Rossini
This paper proposes a novel asymmetric continuous probabilistic score (ACPS) for evaluating and comparing density forecasts. It extends the proposed score and defines a weighted ve…
Public Concern and the Financial Markets during the COVID-19 outbreak
Michele Costola, Matteo Iacopini, Carlo R. M. A. Santagiustina
We measure the public concern during the outbreak of COVID-19 disease using three data sources from Google Trends (YouTube, Google News, and Google Search). Our findings are three-…
Bayesian nonparametric graphical models for time-varying parameters VAR
Matteo Iacopini, Luca Rossini
Over the last decade, big data have poured into econometrics, demanding new statistical methods for analysing high-dimensional data and complex non-linear relationships. A common a…
Nonparametric forecasting of multivariate probability density functions
Dominque Guégan, Matteo Iacopini
The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence struct…
Bayesian Markov Switching Tensor Regression for Time-varying Networks
Monica Billio, Roberto Casarin, Matteo Iacopini
We propose a new Bayesian Markov switching regression model for multidimensional arrays (tensors) of binary time series. We assume a zero-inflated logit regression with time-varyin…
Bayesian Dynamic Tensor Regression
Monica Billio, Roberto Casarin, Matteo Iacopini +1
Tensor-valued data are becoming increasingly available in economics and this calls for suitable econometric tools. We propose a new dynamic linear model for tensor-valued response…
Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox
Roberto Casarin, Matteo Iacopini, Luca Rossini
Discussion on "Sparse graphs using exchangeable random measures" by F. Caron and E. B. Fox. In this discussion we contribute to the analysis of the GGP model as compared to the Erd…