Learn the latest econometric methods to analyse network data

Econometrics of Networks

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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.

The online course consists of three days in which you will practice the material of the lectures in the morning with tutorials in the afternoon using exercises that will be implemented in Matlab.

Read more about the schedule and course readings under 'Additional Course Information' below.

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 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.

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Michael D. König is 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 the 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.

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Course schedule

Please view the course syllabus from 2024. Note that the schedule is subject to change. The final course schedule will be shared with the course participants closer to the date.
Detailed content
Subject to change
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.
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 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
Course readings
All relevant material will be covered in the lecture slides. The slides will be made available to the students on the learning environment before the start of the course. The following literature is complementary to the course slides and covers some additional relevant material for further reading:
- Graham, Bryan, and De Paula, Aureo. Econometric Analysis of Network Data. Elsevier, 2020.
- Kolaczyk, Eric, Statistical Analysis of Network Data: Methods and Models, Springer, 2009.
- LeSage, James, and Robert Kelley Pace. Introduction to Spatial Econometrics. Chapman and Hall/CRC, 2009.
- Bramoulle, Yann, Andrea Galeotti, and Brian Rogers. The Oxford Handbook of the Economics of Networks. Oxford University Press, 2016.
- Jackson, Matthew. O. Social and Economic Networks. Princeton University Press, 2010.
- Carrington, Peter, John Scott, and Stanley Wasserman. Models and Methods in Social Network Analysis, Cambridge University Press, 2005.
Syllabus

Please view the syllabus from 2024 (subject to change)