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Current
Current
Professor of Econometrics at Ca' Foscari University of Venice, since 2019 and Director of the Venice Centre in Economic and Risk Analytics VERA, since 2020.
I am a board member of the: European Society of Bayesian Econometrics ESOBE, the PhD program in Economics SSE, the International Master in Economics and Finance IMEF, the Master in Data Analytics for Business and Societies DABS and the Executive Master in Quantum Machine Learning QML.
Background
Background
I was previously an associate and assistant professor at Ca' Foscari University of Venice, University of Brescia, an adjunct professor at University of Trieste, a research assistant at University of Padova, and a research fellow at GRETA Associates.
I received a Ph.D. in Mathematics (Mathematical Statistics) in 2008, from University Paris Dauphine and CEREMADE-CNRS, a Ph.D. in Economics (Econometrics) in 2003 from the University Ca' Foscari of Venice, a M.Sc. in Applied Mathematics from University Paris Dauphine and ENSAE and a Master in Economics from the University Ca' Focari of Venice. I have been a visiting at the University of Toronto, the University of Paris Sud, the University of Bristol and the University of Paris Dauphine.
Research
Research
It is mainly in Bayesian analysis: Bayesian nonparametrics and semiparametric statistical inference, Monte Carlo simulation methods, time series models, text mining, graphical models, and network analysis. Below is a selection of my research projects. Former and current Ph.D. students at this link.
I am currently an associate editor of Econometrics and Statistics and a research associate of the centres European Centre for Living Technology (ECLT), member of the scientific societies ISBA, IMS, SSC, ES, SIS and SIdE; the scientific projects GRINS; and the research networks EABCN and ENBIS.
Other
Other
Impact of my research: MathSciNet, ArXiv, GoogleScholar, SSRN, ORCID, Scopus, RePEc. Find me in the Math Genealogy Project with an Erdős number of 3.
Spare time: I take pleasure in a variety of hobbies, including gardening, photography and exploring geology with a pleasantly ordinary proficiency. I often embark on geological explorations in stunning locations, accompanied by my family: Diana, Benedetta, and Riccardo.
Tensor Models
- Casarin, R., Radu, C., Wang, Q. (2025), Markov Switching Multiple-equation Tensor Regressions, Journal of Multivariate Analysis, 208, 105427.
- Billio, M., Casarin, R., Iacopini, M. (2024), Bayesian Markov-Switching Tensor Regression for Time-varying Networks, Journal of the American Statistical Association (Theory & Methods), 119(545), 109-121.
- Billio, M., Casarin, R., Kaufmann, S., Iacopini, M. (2023), Bayesian Dynamic Tensor Regression Models, Journal of Business & Economic Statistics, 41(2), 429-439.
Bayesian Nonparametrics and Semi-parametrics
- Casarin, R., Costantini, M., Osuntuyi, A. (2024), Bayesian nonparametric panel Markov-switching GARCH models, Journal of Business & Economic Statistics, 42(1), 135-146.
- Bassetti, F., Casarin, R., Rossini, L. (2020), Hierarchical Species Sampling Models, Bayesian Analysis, 15(3), 809-838.
- Billio, M., Casarin, R., Rossini, L. (2019), Bayesian Nonparametric Sparse VAR Models, Journal of Econometrics, 212(1), 97-115.
- Casarin, R., Molina, G., Ter Horst, E. (2019), A Bayesian Time-Varying Approach to Risk Neutral Density Estimation, Journal of the Royal Statistical Society, Series A, 182 (1), 165-195.
- Bassetti, F., Casarin, R., Ravazzolo, F. (2018), Bayesian Nonparametric Calibration and Combination of Predictive Distributions, Journal of the American Statistical Association (Theory & Methods), 113(522), 675-685.
- Bassetti, F., Casarin, R. and Leisen, F. (2014), Beta-product Dependent Pitman-Yor Process Prior for Bayesian Inference, Journal of Econometrics, 180, 49-72.
Non-Gaussian Models
- Baltodano, O., Bassetti, F., Carallo, G., Casarin, R. (2025), First-order integer-valued autoregressive processes with Generalized Katz innovations, Econometrics and Statistics, forthcoming
- Carallo, G., Casarin, R., Robert C.P. (2024), Generalized Poisson Difference Autoregressive Processes, International Journal of Forecasting, 40(4), 1359-1390.
- Bormetti, G., Casarin, R., Corsi, F. and Livieri, G. (2020), A stochastic volatility model with realized measures for option pricing, Journal of Business & Economic Statistics, 38(4), 856-871.
- Casarin, R. (2014), Comment on a Tractable State-Space Model for Symmetric Positive-Definite Matrices, Bayesian Analysis, 9(4), 793-804.
- Casarin, R., Dalla Valle, L. and Leisen, F. (2012), Bayesian Model Selection for Beta Autoregressive Processes, Bayesian Analysis,7(1), 1-26.
Random Fields
- Casarin, R., Leisen, F., Molina, G. and Ter Horst, E. (2015), Beta Markov Random Field Calibration of the Term Structure of Implied Risk Neutral Densities, Bayesian Analysis, 10(4), 791- 819.
Graphical Models
Markov Switching Models
Model Combination and Calibration
Monte Carlo Methods
- Bianchi, D., Billio, M., Casarin, R., Guidolin, M. (2019), Modeling Systemic Risk with Markov Switching Graphical SUR Models, Journal of Econometrics, 210(1), 58-74.
- Ahelegbey D. F., Billio, M. and Casarin, R. (2016), Bayesian Graphical Models for Structural Vector Autoregressive Processes, Journal of Applied Econometrics, 31(2), 357-386.
Markov Switching Models
- Casarin, R., Peruzzi, A., Steel, F.J. (2025), Media Bias and Polarization through the Lens of a Markov Switching Latent Space Network Model, The Annals of Applied Statistics, forthcoming.
- Agudze, K. M., Billio, M., Casarin, R., Ravazzolo, F. (2022), Markov Switching Panel with Endogenous Synchronization Effects, Journal of Econometrics, 230(2), 281-298.
- Casarin, R., Foroni, C., Marcellino, M., Ravazzolo, F. (2018), Economic Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model, The Annals of Applied Statistics, 12(4), 2559-2568.
- Casarin, R., Sartore, D. and Tronzano, M. (2018), A Bayesian Markov-switching correlation model for contagion analysis on exchange rate markets, Journal of Business & Economic Statistics, 36(1), 101-114.
- Billio, M., Casarin, R., Ravazzolo, F. and van Dijk, H.K. (2016), Interconnections between Eurozone and US booms and busts using a Bayesian Panel Markov-Switching VAR model, Journal of Applied Econometrics, 31(7), 1352-1370.
Model Combination and Calibration
- Bernardi, M., Casarin, R., Petrella, L., Maillet, B. (2024), Bayesian Dynamic Quantile Model Averaging, Annals of Operations Research, forthcoming.
- Casarin, R., Grassi, S., Ravazzolo, F. and van Dijk, H.K. (2023), A Flexible Predictive Density Combination for Large Financial Data Sets in Regular and Crisis Periods, Journal of Econometrics, 237(2), 1-12.
- Billio, M. and Casarin, R., Ravazzolo, F. and Van Dijk, H.K. (2013), Time-varying Combinations of Predictive Densities using Nonlinear Filtering, Journal of Econometrics, 177(2), 213-232.
Monte Carlo Methods
- Casarin, R., Frattarolo, L., Radu, C., Robert, C. P. (2024), Living on the Edge: An Unified Approach to Antithetic Sampling, Statistical Science, 39(1), 115-136.
- Casarin, R., Maillet, B., Osuntuyi, A. (2024), Monte Carlo within Simulated Annealing for Integral Constrained Optimizations, Annals of Operations Research, 334, 205–240.
- Martino, L., Casarin, R., Leisen, F., Luengo, D. (2018), Adaptive Independent Sticky MCMC algorithms, EURASIP Journal on Advances in Signal Processing, 5, 1-28.
- Casarin, R., Craiu, R. and Leisen, F. (2016), Embarrassingly Parallel Sequential Markov-chain Monte Carlo for Large Sets of Time Series, Statistics and Its Interface, 9(4), 497-508.
- Casarin, R., Leisen, F. and Craiu, R. (2013), Interacting Multiple Try Algorithms with Different Proposal Distributions, Statistics and Computing, 23(2), 185-200.
- Casarin, R. and Marin, J.-M., (2009), Online data processing: Comparison of Bayesian regularized particle filters, Electronic Journal of Statistics, 3, 239-258.
On Evidence
Casarin, R., Craiu, R., Wang, Q. (2025). Markov Switching Tensor Regressions.
In Aneiros, G., Bongiorno, E.G., Goia, A., Hušková, M. (Eds), New Trends in Functional Statistics and Related Fields. IWFOS 2025. Contributions to Statistics. Springer Verlag, Cham
In Aneiros, G., Bongiorno, E.G., Goia, A., Hušková, M. (Eds), New Trends in Functional Statistics and Related Fields. IWFOS 2025. Contributions to Statistics. Springer Verlag, Cham
Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2022), Matrix-variate Smooth Transition Models for Temporal Networks.
In Bekker, A., Arashi, M., Chen, D. and Ferreira, J. (Eds.) Innovations in Multivariate Statistical Modelling: Navigating Theoretical and Multidisciplinary Domains, Springer Emerging Topics in Statistics and Biostatistics, Springer Verlag.
In Bekker, A., Arashi, M., Chen, D. and Ferreira, J. (Eds.) Innovations in Multivariate Statistical Modelling: Navigating Theoretical and Multidisciplinary Domains, Springer Emerging Topics in Statistics and Biostatistics, Springer Verlag.
Bassetti, F., Casarin, R., Del Negro, M., (2023) Inference on Probabilistic Surveys in Macroeconomics with an Application to the Evolution of Uncertainty in the Survey of Professional Forecasters during the COVID Pandemic.
In van der Klaauw, W., Topa, G. and Bachmann, R. (Eds.), Handbook of Economic Expectations, Elsevier.
In van der Klaauw, W., Topa, G. and Bachmann, R. (Eds.), Handbook of Economic Expectations, Elsevier.
Casarin, R. and Veggente, V. (2021), Random Projection Methods in Economics and Finance.
In Petr, H., Uddin, M.M., and Abedin, M. Z. (Eds.), The Essentials of Machine Learning in Finance and Accounting, Chapter 6, Routledge, Taylor & Francis.
In Petr, H., Uddin, M.M., and Abedin, M. Z. (Eds.), The Essentials of Machine Learning in Finance and Accounting, Chapter 6, Routledge, Taylor & Francis.
Casarin, R., Corradin, F., Peruzzi, A. (2025), ISP Index: A Parsimonious Method to Predict Defaults.
In D'Ambrosio, A., de Rooij, M., De Roover, K., Iorio, C., La Rocca, M. (eds), Supervised and Unsupervised Statistical Data Analysis. CLADAG-VOC 2025. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham.
In D'Ambrosio, A., de Rooij, M., De Roover, K., Iorio, C., La Rocca, M. (eds), Supervised and Unsupervised Statistical Data Analysis. CLADAG-VOC 2025. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham.
Bassetti, F., Casarin, R. and Ravazzolo, F. (2020), Density Forecasting.
In Fuleky, P. (Ed.), Macroeconomic Forecasting in the Era of Big Data, Springer Verlag, 483-507.
In Fuleky, P. (Ed.), Macroeconomic Forecasting in the Era of Big Data, Springer Verlag, 483-507.
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