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Learn about recent econometric methods to analyze network data

Students will learn how to analyse the role of networks in various social and economic environments.

Course description

Learn about recent econometric methods to analyse network data.

Networks play an increasingly dominant role in many social, business, and economic environments. Moreover, network data becomes increasingly relevant and available due to the rise of online social media and digitisation. 

Upon successful completion of the course, students will:

  • become acquainted with different statistical methodologies for analysing networks while learning how to see these different methodologies complementing each other.
  • learn to model network problem situations mathematically, and adapt the methods learned to new situations at hand.
  • be able to recognise, understand, and analyse societal and business problems in which networks are central.
  • learn how networks affect supply and demand in markets, how this leads to market failures, and how government policies can address these.

Our registration deadline has passed, it is no longer possible to apply. You can leave your contact details to be added to our summer 2023 newsletter. 

About this course

Course level

  • Master / Advanced

Course coordinator

  • Michael König


  • 2 ECTS

Contact hours

  • 20


  • English

Tuition fee

  • €375 - €650

Additional course information

  • Content guide

    1. Examples of Networks and Data 

    2. Network Statistics, Visualization and Graphs

    • Elements of Graph Theory 

    • Graphs and Matrices 

    • Bipartite Graphs 

    • Core-periphery Networks and Nested Split Graphs 

    • Network Statistics: Average path length, clustering and assortativity 

    • Centrality in Networks: Degree, eigenvector, Katz-Bonacich centrality and Google's Page Rank 

    • Network Visualization: Force-directed, circular and layered layout 

    3. Econometrics of Interactions in Networks

    • Spatial Autoregressive (SAR) Model 

    • Linear Quadratic Utility 

    • Endogeneity of the Spatial Lag 

    • Two-Stage Least Squares (2SLS) 

    • Maximum Likelihood Estimation (MLE) 

    • Identification Issues 

       – Correlated Effects, Sorting and Selection 

       – Endogenous Link Formation 

    • Multiple Spatial Weight Matrices 

    • Spatial Panel Data

    4. Econometrics of Network Formation

    • Exponential Random Graph Model (ERGM) 

    • Conditional Edge-Independence 

       – Erdös-Rényi Random Graph 

       – Logistic Regression 

       – Unobservable Characteristics (beta-model) 

       – Tetrad Logit Estimator 

    • Random Utility Model 

    • Maximum Likelihood Estimation (MLE) 

    • Markov Chain Monte Carlo 

       – Gibbs Sampling 

       – Metropolis Hastings Algorithm 

    • Stochastic Block Model (SBM) 

    • Temporal ERGM

    5. Joint Estimation of Outcomes and Network Formation

    5.1. Coevolution of Networks and Behavior: An application to R&D collaboration networks

    • Structural Model: Utility and the potential game     

    • Estimation     

       – Computational Problem and the Exchange Algorithm     

       – Double Metropolis-Hastings (DMH) Algorithm     

       – Unobserved Heterogeneity  

    • Empirical Illustration: R&D collaborations     

    5.2. Network Formation with Multiple Activities: An application to team production and co-authorship networks  

    • Bipartite Network, Production Function, and Utility

    • Equilibrium Characterization and Line Graphs  

    • Estimation with Endogenous Matching

    • Empirical Illustration: Co-authorship networks

    6. Spatial Modeling Approach for Dynamic Network Formation and Interactions

    • Spatial Dynamic Panel Data (SDPD) Model

    • A General Dynamic Network Formation Model

    • Combining SDPD with the Network Formation Model: Joint likelihood function

    • An Empirical Application to Peer Effects in Academic Performance

    7. Big Data Meets Networks 

    • The Digital Layer: How innovative firms relate on the Web

    • Automated Robot for Generic Universal Scraping (ARGUS) 

    • Input, Interface and Output of ARGUS 

    • Sectoral Hyperlink Network 

    • Hyperlink Types

  • Course schedule

    Classes will take place from Monday 18 July until Friday 22 July. In general classes will be during the week between 9am and 17pm (please take exceptions into account). Wednesday afternoon will be off for optional social activities or personal time. Good to be aware that self-study will be required in your private time (nights and weekends). Sunday 17 July and Sunday 24 July are arrival and departure days (in case you arrange accommodation via our housing service). More details will be shared in the course syllabus which will be shared with the participants in June. 

  • About the course coordinator

    Michael D. König is an associate professor at the Department of Spatial Economics at VU Amsterdam. He is also a research fellow at the Tinbergen Institute, the Centre for Economic Policy Research (CEPR) and the Swiss Economic Institute (KOF). Prior to joining VU Amsterdam he was a senior research associate at the University of Zurich, a visiting scholar at Bocconi University, the Stanford Institute for Economic Policy Research (SIEPR) and the Department of Economics at Stanford University.

Team VU Amsterdam Summer School

We are here to help!

+31 20 59 86429

Skype: by appointment via


  • Bianca
  • Programme Coordinator
  • Celia
  • Summer and Winter School Officer
Celia VU Amsterdam Summer & Winter School
  • Helena
  • Summer and Winter School Support Assistant
Helena VU Amsterdam Summer and Winter School