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.
Under the Italian PRIN Project entitled “Hi-di network econometric analysis of high dimensional models with network structures in macroeconomics and finance – PROT. 2017TA7TYC” funded by the Italian Ministry of Education, the Departments of Economics at the University of Bologna, the Department of Economics of the Ca’ Foscari University of Venice and the Faculty of Economics and Management at Free University of Bozen-Bolzano are opening a call of interest for three positions as Research Fellow (one position for each university).
The positions will be funded by the project up to 24 months.
The research project covers the following topics:
- Development of novel multivariate econometric models and methods able to deal with network effects and to take into account time varying relationships. Attention will be paid on how latent factors drive economic systems and are subject to instability. Their roles will be studied and compared in the pre- and post-coronavirus era.
- Analysis of interconnected networks: either physical or intangible networks, observed or estimated. Results will expand academic knowledge on statistical modelling of network event data in a broad range of areas including, but not limited to, social media information diffusion, economic/financial crises contagion and epidemic/disease spread phaenomena.
- Economic impact of uncertainty: uncertainty is expected to act as a key driving factor in the aftermath of the covid-19 shock and the focus will be also on the policy actions necessary at the macro level to offset the economic consequences of a shock of size never experienced by world economies after WW2.
Interested researchers can express their willingness to apply as candidates for the above positions through the link below not later than 31st May 2020. Applications should include:
- a CV of the candidate;
- a short research proposal related to the topics covered by the project;
- and the indication of the University where the research activity will be carried out.
For more information about the call of interest, please contact:
- Monica Billio ( firstname.lastname@example.org) - Department of Economics of the Ca’ Foscari University of Venice;
- Giuseppe Cavaliere ( email@example.com) - Departments of Economics at the University of Bologna;
- Francesco Ravazzolo ( firstname.lastname@example.org) - Faculty of Economics and Management at Free University of Bozen-Bolzano.
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
Last update: 25/05/2020