Statistical and Econometric Analysis of Network Data

About this course

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 Behaviour: 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 Modelling 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

• Learning objectives

  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 recognize, understand, and analyse societal and business problems in which networks are central.

  • learn how networks affect demand and supply in markets, how this leads to market failures, and how government policies can address these.

• 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.

• Course syllabus

  Here you can download the preliminary syllabus for the summer course 2024. 

  *Please note that this is the preliminary syllabus, and it still might be subject to change.