Peer-Reviewed Journals and Book Chapters

  1. Bonaccolto, G., Caporin, M. and Iacopini, M. (2024), "Extreme time-varying spillovers between high carbon emission stocks, green bond and crude oil: Comment", Energy Economics, 132:107469 -- (article)
  2. Iacopini, M., Poon, A., Rossini, L. and Zhu, D. (2023), "Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP", Journal of Economic Dynamics and Control, 157:104757 -- (article)
  3. Costola, M. and Iacopini (2023), "Measuring sovereign bond fragmentation in the Eurozone", Finance Research Letters, 51:103354 -- (article)
  4. Billio, M., Casarin, R. and Iacopini, M., (2024), "Bayesian Markov switching tensor regression for time-varying networks", Journal of the American Statistical Association, 119(545), 109--121 -- (article)
  5. Billio, M., Casarin, R., Iacopini, M. and Kaufmann, S. (2023), "Bayesian dynamic tensor regression", Journal of Business and Economic Statistics, 41(2):429--439 -- (article)
  6. Iacopini, M., Ravazzolo, F. and Rossini, L. (2023), "Proper scoring rules for evaluating density forecasts with asymmetric loss functions", Journal of Business and Economic Statistics, 41(2):482--496 -- (code; article)
  7. Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2022), "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, pages 137--167, Springer Emerging Topics in Statistics and Biostatistics
  8. Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2021), "COVID-19 spreading in financial networks: A semiparametric matrix regression model", Econometrics and Statistics, 29, 113--131 -- (article)
  9. Costola, M., Iacopini, M. and Santagiustina, C.R.M.A. (2021), "On the "mementum" of meme stocks", Economics Letters, 207, 110021 -- (article)
  10. Billio, M., Casarin, R., Costola, M. and Iacopini, M. (2021), "A matrix-variate t model for networks", Frontiers in Artificial Intelligence 4, 49 -- (article)
  11. 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 -- (article)
  12. 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 -- (article)
  13. 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.
  14. 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 -- (article)
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. 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.

Other publications

  1. Iacopini, M., (2016), "Basics of optimization theory with applications in MATLAB and R", Quaderni di didattica, Department of Economics, Ca' Foscari University of Venice.

Working papers

  1. Iacopini, M., Poon, A., Rossini, L. and Zhu, D. "A Quantile Nelson-Siegel model" -- (working paper)
  2. Glocker, C., Iacopini, M., Krisztin, T. and Piribauer, P. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers" -- (working paper)
  3. Costola, M., Iacopini, M. and Wichers, C. "Bayesian SAR model with stochastic volatility and multiple time-varying weights" -- (working paper)
  4. Iacopini, M., O'Neill, E. and Rossini, L. "Static and dynamic BART for rank-order data" -- (working paper)
  5. Bassetti, F., Casarin, R. and Iacopini, M. "A spatiotemporal gamma shot noise Cox process" -- (working paper)
  6. Pintado, M.F., Iacopini, M., Rossini, L. and Shestopaloff, A.Y. "Uncertainty quantification in Bayesian reduced-rank sparse regressions" -- (working paper)
  7. Iacopini, M., Ravazzolo, F. and Rossini, L. "Bayesian multivariate quantile regression with alternative time-varying volatility specifications" -- (working paper)
  8. Iacopini, M., Poon, A., Rossini, L. and Zhu, D. "Money Growth and Inflation: A Quantile Sensitivity Approach" -- (working paper)
  9. Bianchi, D., Iacopini, M. and Rossini, L., "Stablecoins and cryptocurrency returns: Evidence from large Bayesian VARs" -- (working paper)
  10. Guégan, D. and Iacopini, M., "Nonparametric forecasting of multivariate probability density functions" -- (working paper)
  11. Iacopini, M. and Rossini, L., "Bayesian nonparametric graphical models for time-varying parameters VAR" -- (working paper)
  12. Iacopini, M. and Santagiustina, C.R.M.A., "Visualizing and comparing distributions with half-disk density strips" -- (code; working paper)