The Italian Econometric Association (SIdE-IEA) in collaboration with the Venice centre in Economic and Risk Analytics for Public Policies (VERA)  organizes the course for PhD students in:

Network Econometrics

1 - 5 July, 2024
Università Ca' Foscari  Venezia 


The Summer School aims to provide participants with models and tools from graph theory to analyse various effects of social, economic, and political interaction. The school will host leading scholars developing relevant network modelling and inference research and their applications to various fields. It is organised by the Department of Economics in collaboration with the Venice Center for Risk Analytics for Public Policies (VERA) , the Ca’ Foscari International College and the Italian Econometrics Association (SIdE). The school and the participants from the Italian Advanced Schools are financially supported by the Ca’ Foscari International College under the aegis of ASSI (Alliance of the Italian Advanced Schools).


Roberto Casarin

Address: Dept. of Economics, University Ca' Foscari of Venice,   San Giobbe 873/b, 30121 Venezia, Italy
Phone:    +39 



The maximum number of allowed participants in presence is 20.

Open seats available. For more info email to


Intermediate knowledge of statistical inference and econometrics


Course Outline

1. Graph Theoretic Foundation of Networks

    1.1 Definitions

    1.2 Graph Connectivity

    1.3 Multilayer-networks

    1.4 Tutorial 1: Introduction to R (Data manipulation and regression)

    1.5 Tutorial 2: Network mapping and visualization in R

    1.6 Tutorial 3: Text mining and visualization in R


2. Network Extraction Methods

    2.1 Graphical Models

    2.2 Parametric sparse regression models

    2.3 Nonparametric sparse regression models

    Tutorial 3: Extraction of Financial Networks in Matlab

    Tutorial 4: Network visualization with Gephi


3. Temporal Network Models

     3.1 Tensor decomposition

     3.1 Dynamic Tensor Models

     3.2 Markov-switching Tensor Models

     Tutorial 6: Application to COMTRADE and Financial Networks in Matlab


4. Multi-layer Network Models

    4.1 Definition and analysis

    4.2 Exctraction

   Tutorial 7: Application to Oil Linkages Networks in Matlab


5. High-Dimensional Network Inference

5.1 Gaussian graphical model

5.2 Regularized inference for a Gaussian graphical model

Tutorial 8: Graphical lasso

Tutorial 9: Network inference under missing data and with covariate

Tutorial 10: Gaussian Copula graphical models


Reference textbooks and suggested readings:

Introductory references

  • Jackson, M.O. (2008) Social and Economic Networks, Priceton University Press.
  • Diebold, F. and Yilmaz, K. (2015), Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring, Oxford University Press.
  • Bollobas, B. (1998), Modern Graph Theory, Springer.

 Further references

  • Jensen, F. (1996), An Introduction to Bayesian Networks, Springer-Verlag
  • Lauritzen, S. (1996). Graphical Models, Oxford University Press
  • Pearl, J. (1998). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference.
  • Whittaker, H. (1990). Graphical Models in Applied Multivariate Statistics, John Wiley.



Participants will use their laptops with MATLAB, R and GEPHI already installed on them.


Venue and timetables

The Course requires a full-time attendance and participation is not compatible with other jobs at the same time (e.g. preparation of other exams). Lectures and tutorials will be in English, with the following schedule (provisional):

  • Monday to Friday: lectures: 9.30-13.00, 15.00-18.30.

The course will be held in the Campus Economico San Giobbe at Università Ca’ Foscari, Venezia, Italy. Address: Dipartimento di Scienze Economiche - S. Giobbe, 873 - 30121 Venezia.

Participants will be hosted at Camplus Santa Marta, Calle Larga S. Marta, 2137, 30123 Venezia VE