Research Papers

WP 1:

Iacopini, M. and Rossini, L. (2019) - Bayesian nonparametric graphical models for time-varying parameters VAR. Available at arXiv: 1906.02140 ​

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 approach for addressing dimensionality issues relies on the use of static graphical structures for extracting the most significant dependence interrelationships between the variables of interest. Recently, Bayesian nonparametric techniques have become popular for modelling complex phenomena in a flexible and efficient manner, but only few attempts have been made in econometrics. In this paper, we provide an innovative Bayesian nonparametric (BNP) time-varying graphical framework for making inference in high-dimensional time series. We include a Bayesian nonparametric dependent prior specification on the matrix of coefficients and the covariance matrix by mean of a Time-Series DPP as in Nieto-Barajas et al. (2012). Following Billio et al. (2019), our hierarchical prior overcomes over-parametrization and over-fitting issues by clustering the vector autoregressive (VAR) coefficients into groups and by shrinking the coefficients of each group toward a common location. Our BNP timevarying VAR model is based on a spike-and-slab construction coupled with dependent Dirichlet Process prior (DPP) and allows to: (i) infer time-varying Granger causality networks from time series; (ii) flexibly model and cluster non-zero time-varying coefficients; (iii) accommodate for potential non-linearities. In order to assess the performance of the model, we study the merits of our approach by considering a well-known macroeconomic dataset. Moreover, we check the robustness of the method by comparing two alternative specifications, with Dirac and diffuse spike prior distributions.

WP 2:

Hong, H. and Rossini, L. (2020) - Bayesian GAS network Model.

OTHER RESEARCH PROJECTS

• Foroni, C., Ravazzolo, F and Rossini, L. (2020) - Are low frequency macroeconomic variables important for high frequency electricity prices. Available at arXiv 2007:13566 (R&R). Previous version entitled “Forecasting daily electricity prices with monthly macroeconomic variables” at ECB Working Paper No 2250

• Gianfreda, A., Ravazzolo, F. and Rossini, L. (2020) - Large Time-Varying Volatility Models for Electricity Prices. Available at CAMP Working Paper Series 05/2020 (submitted) ​​
• Huber, F. and Rossini, L. (2020) - Inference in Bayesian Additive Vector Autoregressive Tree Models. Available at arXiv: 2006.16333 (R&R) ​​
• Iacopini, M., Ravazzolo, F. and Rossini, L. (2020) - Proper Scoring Rules for evaluating asymmetry in density forecasting. Available at arXiv: 2006.11265 (R&R) ​​
• Bianchi, D., Rossini, L. and Iacopini, M. (2020) - Stable Coins and Cryptocurrency Returns: Evidence from Large Bayesian VARs. Available at SSRN 3605451 (Submitted) ​​
• Durante, F., Gianfreda, A., Ravazzolo, F. and Rossini, L. (2020) - Does Electricity Price depend on Renewable Energy? A Multivariate Dependence Analysis. (Submitted) ​​
• Dalla Valle,L., Leisen, F., Rossini, L. and Zhu, W. (2020) - A Pòlya-Gamma Sampler for a Generalized Logistic Regression. Available at arXiv:1909.02989 (R&R) - R Code ​
• Bassetti, F., Casarin, R. and Rossini, L. (2020) - Hierarchical Species Sampling Models. Bayesian Analysis, 15:3, 809-838 ​​
• Gianfreda, A., Ravazzolo, F. and Rossini, L. (2020) - Comparing the Forecasting Performance of Linear Models for Electricity Prices with High RES Penetration. International Journal of Forecasting, 36:3, 974-986 ​​
• Leisen, F., Rossini, L. and Villa, C. (2020) - Loss-based approach to two-piece location-scale distributions with applications to dependent data. Statistical Methods & Applications, 29, 309-333 ​​
• Dalla Valle, L., Leisen, F., Rossini, L. and Zhu, W. (2020) - Bayesian Analysis of Immigration in Europe with Generalized Logistic Regression. Journal of Applied Statistics, 47:3, 424-438 ​​​
• Bohte, R. and Rossini, L. (2019) - Comparing the Forecasting of Cryptocurrencies by Bayesian Time-Varying Volatility Models. Journal of Risk and Financial Management, 12:3, 150 ​​