Econometric Analysis of High Dimensional Network Structures in Macroeconomics and Finance
High dimensional modelling and large dataset handling have gained attention in Economics and Finance, given also the recent surge of publicly available data. One of the key challenges of high-dimensional models is the complex interactions among variables and the inferential difficulty associated with handling large datasets.
The project Hi-Di NET deals with the 3 key aspects for forecasting and structural analysis:
- network effects and interconnectedness;
- time variation in the relationships;
- large cross section of variables and high dimension databases.
It is organised in 3 WPs dealing with:
- inclusion of network into time series analysis to deal with dynamics and time dependence;
- inference on observed and latent networks and identification issues;
- use of large datasets and related computational challenges.
The aim is to develop novel multivariate econometric models and efficient methods suitable for high dimension databases and able to deal with network effects and time varying relationships.
From an applied perspective, the Hi-Di NET project will deal with the central theme of financial and macroeconomic stability, declined in 3 empirical vertical streams related to highly relevant topics: systemic risk, uncertainty impact and new fintech instruments.
Roberto Casarin, Full Professor of Econometrics, Department of Economics, Ca' Foscari University of Venice
Massimiliano Giuseppe Marcellino, Full Professor of Econometrics, Department of Economics, Bocconi University
Michele Costola, Research Fellow in Economic Policy, Department of Economics, Ca' Foscari University of Venice
Jan Ditzen, Research Fellow in Econometrics, Department of Economics and Management, University of Bozen
- Agudze K. M., Billio M., Casarin R., Ravazzolo F., 2021, Markov Switching Panel with Endogenous Synchronization Effects, Journal of Econometrics
- Billio M., Frattarolo L., Guégan D., 2021, Multivariate Radial Symmetry of Copula Functions: Finite Sample Comparison in the i.i.d Case, Dependence Modeling, vol. 9, 1, pp 43-61
- Billio M., Caporin M., Frattarolo L., Pelizzon L., 2021, Networks in risk spillovers: A multivariate GARCH perspective, Econometrics and Statistics
- Billio M., Casarin R., Costola M., Iacopini M., 2021, A Matrix-Variate t Model for Networks, Frontiers in Artificial Intelligence
- Boswijk P., Cavaliere G., Georgiev I., Rahbek A., 2021, Bootstrappingnon-stationary stochastic volatility, Journal of Econometrics, vol. 224, 1, pp. 161-180
- Caporin M., Gupta R., Ravazzolo F., 2021, Contagion between Real Estate and Financial Markets: A Bayesian Quantile-on-Quantile Approach, North American Journal of Economics and Finance, vol. 55
- Tebaldi C., 2021, Self-Organized Criticality in Economic Fluctuations: The Age of Maturity, Frontiers in Physics
Last update: 27/01/2022