Dr Matteo Iacopini

Profile

Dr Iacopini is a Lecturer in Statistics at the Queen Mary University of London. Before joining Queen Mary University London, he was a postdoctoral researcher at the Vrije Universiteit Amsterdam in the Netherlands.

Dr Iacopini's primary area of expertise is in Bayesian methods for complex and multi-dimensional (i.e., tensor) data. These data structures naturally arise, for example, in 3D brain imaging or when multiple features (1st dim) of individuals (2nd dim) are observed across several types (3rd dim). All these datasets are characterised by an intrinsic structure and multiple dimensions, which are challenging to deal with in standard multivariate statistical models. The Bayesian approach offers a suitable environment for making valid inferences in this framework.

Dr Iacopini's research focuses on developing Bayesian statistical models and computational methods for time series of complex and multi-dimensional data. He is also passionate about designing methods for dynamic multilayer networks and time series of counts.

Research

Research Interests:

 Bayesian statistics

• Models for time series data
• Bayesian Nonparametrics
• Shrinkage priors and sparsity-inducing techniques
• Mixture models
• Models for count data

Tensors

• Models for Multi-dimensional data (aka tensors)
• Tensor calculus and tensor decompositions
• Tensor modelling in statistics

Networks

• Time-varying networks
• Multilayer networks
• Network estimation

 

Main applications to:

• Multivariate time series in economics and finance;
• Networks in economics, finance, and sociology
• Social media data

Publications

• Costola, M. and Iacopini (2022), "Measuring sovereign bond fragmentation in the Eurozone", Finance Research Letters, (forthcoming)
• Billio, M., Casarin, R. and Iacopini, M. (2022), "Markov Switching Tensor Regression for Time-varying Networks", Journal of the American Statistical Association (forthcoming)
• Billio, M., Casarin, R., Iacopini, M. and Kaufmann, S. (2022), "Bayesian dynamic tensor regression", Journal of Business and Economic Statistics (forthcoming)
• Iacopini, M., Ravazzolo, F. and Rossini, L. (2022), "Proper scoring rules for evaluating density forecasts with asymmetric loss functions", Journal of Business and Economic Statistics, (forthcoming)
• Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2021), "Matrix-variate Smooth Transition Models for Temporal Networks", in Arashi, M., Bekker, A., Che, D., and Ferreira, J., Innovations in Multivariate Statistical Modeling: navigating theoretical and multidisciplinary domains, chapter XX, pages XX, Springer Emerging Topics in Statistics and Biostatistics. (forthcoming)
• Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2021), "COVID-19 spreading in financial networks: A semiparametric matrix regression model", Econometrics and Statistics, (forthcoming)
• Costola, M., Iacopini, M. and Santagiustina, C.R.M.A. (2021), "On the "mementum" of meme stocks", Economics Letters, 207, 110021
• Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2021), "A matrix-variate t model for networks", Frontiers in Artificial Intelligence 4, 49
• Iacopini, M. and Santagiustina, C.R.M.A. (2021), "Filtering the intensity of public concern from social media count data with jumps", Journal of the Royal Statistical Society: Series A, 184:1283--1302
• Costola, M., Iacopini, M. and Santagiustina, C.R.M.A. (2020), "Google search volumes and the financial markets during the COVID-19 outbreak", Finance Research Letters, 42:101884
• Iacopini, M., Ravazzolo, F. and Rossini, L. (2020), "A discussion on: On a Class of Objective Priors from Scoring Rules by F. Leisen, C. Villa and S. G. Walker", Bayesian Analysis, 15(4):1392--1393.
• Casarin, R., Iacopini, M., Molina, G., ter Horst, E., Espinasa, R., Sucre, C. and Rigobon, R. (2020), "Multilayer Network Analysis of Oil Linkages", The Econometrics Journal, 23(2):269--29
• Tonellato, S. and Iacopini, M. (2018), "A discussion on: Using stacking to average Bayesian predictive distributions by Y. Yao, A. Vehtari, D. Simpson and A. Gelman", Bayesian Analysis, 13(3):994--996.
• Billio, M., Casarin, R. and Iacopini, M. (2018), "Bayesian tensor regression models", In Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2018. Eds. Corazza, M., Durbán, M., Grané, A., Perna, C., Sibillo, M., Springer, 149--153.
• Billio, M., Casarin, R. and Iacopini, M. (2018), "Bayesian tensor binary regression", In Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2018. Eds. Corazza, M., Durbán, M., Grané, A., Perna, C., Sibillo, M., Springer, 143--147.
• Casarin, R., Iacopini, M. and Rossini, L. (2017), "A discussion on: Sparse graphs using exchangeable random measures by F. Caron and E. B. Fox", Journal of the Royal Statistical Society: Series B, 79(5):51--53.
• Billio, M., Casarin, R. and Iacopini, M. (2017), "Bayesian tensor regression models", In Proceedings of the Conference of the Italian Statistical Society. Statistics and Data Science: new challenges, new generations. Eds. Alessandra Petrucci and Rosanna Verde, Firenze University Press, 179--186.

 

For a full list of publications please visit my personal webpage.