Vrije University - Summer graduate programs
Summer Course in Statistical and Econometric Analysis of Network DataAmsterdam, Netherlands
DURATION
2 Weeks
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
Request application deadline
EARLIEST START DATE
21 Jul 2025
TUITION FEES
EUR 545 *
STUDY FORMAT
On-Campus
* VU students
Key Summary
Introduction
Students will learn how to analyze the role of networks in various social and economic environments.
Learn about recent econometric methods to analyze network data
Networks play an increasingly dominant role in many social, business, and economic environments. Moreover, network data has become increasingly important and available due to the rise of online social media and digitization.
The course will combine online lectures with hands-on empirical and programming exercises.
Course Overview
- Course level: Advanced Bachelor's and Master's
- Course curriculum: read more about the course curriculum
- Coordinating lecturer: Michael D. König
- Forms of assessment: Tutorials and exercises
- Contact hours: 20
Gallery
Admissions
Scholarships and Funding
Equal Access Scholarship
Application Procedure
Application for the Equal Access Scholarship will open in Febraury
Great that you are interested in applying for the Equal Access Scholarship. You can apply to the scholarship between 12 February and 1 April. Please be aware that it is only possible to select one course.
The results of the scholarship selection will be announced in May. Since we have a limited number of scholarships available for a large number of applicants, we suggest - if possible! - to complete your payment at the time of your course application to guarantee your place in the course. However, if you are not able to come without the scholarship, you can just wait until the announcement. If you would like to come, regardless of whether you will be granted the scholarship, it is best to secure your place in the course by completing your payment via our regular application form. If the scholarship is granted to you, the tuition and accommodation fees will be reimbursed.
Deadline to submit your Equal Access Scholarship application: 31 March (23:59 CET).
Requirements
When you apply via the Equal Access Scholarship application form you will be requested to upload the following documents:
- Curriculum Vitae/Résumé (CV) stating your educational background.
- Professional Letter of Reference Including:
- His/her/their experience working with you (either in an academic, professional, or volunteer setting)
- His/her/their motivation for recommending you for the scholarship
- Complete contact information
- His/her/their experience working with you (either in an academic, professional, or volunteer setting)
- His/her/their motivation for recommending you for the scholarship
- Complete contact information
- When filling out the scholarship form, we will ask the following questions*:
- Why are you interested in joining VU Amsterdam Summer School?
- What’s your motivation for selecting this course?
- How you will use the information you learn to make a positive impact in the future for both you and your community?
- Why do you deserve this scholarship?
- Why are you interested in joining VU Amsterdam Summer School?
- What’s your motivation for selecting this course?
- How you will use the information you learn to make a positive impact in the future for both you and your community?
- Why do you deserve this scholarship?
Please stick to a maximum of 150 words per question.
Green Travel Grant
At VU Amsterdam Summer School we are also committed to VU's sustainability goals and we aim to reduce the environmental impact of mobility, and specifically, student travel. Therefore, we are thrilled to offer Green Travel Grants to encourage sustainable travel for students attending our summer school.
Where can I apply?
Once the courses have been confirmed to run in mid-May or June, we will send out a newsletter to our participants with a link where they can apply for either funding for train travel or funding for bus travel.
The application period will last two weeks, and we will select the winners via a lottery system. More information on the specific deadlines can be found in the newsletter we send out in May.
How does it work?
For students to receive the economic compensation they will need to submit their purchased travel tickets via email within two weeks after being selected as winners of the grant. Once the deadline to submit their tickets has passed, the students will receive the reimbursement.
Curriculum
- Examples of Networks and Data
- 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
- 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
- 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
- Correlated Effects, Sorting, and Selection
- Endogenous Link Formation
- Multiple Spatial Weight Matrices
- Spatial Panel Data
- 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
- 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
- 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
- Gibbs Sampling
- Metropolis-Hastings Algorithm
- Stochastic Block Model (SBM)
- Temporal ERGM
- 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
- 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
- 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
- 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
- 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
- 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
- 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 on Academic Performance
- 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 on Academic Performance
- Big Data Meets Networks
- The Digital Layer: How Innovative Firms Relate to the Web
- Automated Robot for Generic Universal Scraping (ARGUS)
- Input, Interface, and Output of ARGUS
- Sectoral Hyperlink Network
- Hyperlink Types
- The Digital Layer: How Innovative Firms Relate to the Web
- Automated Robot for Generic Universal Scraping (ARGUS)
- Input, Interface, and Output of ARGUS
- Sectoral Hyperlink Network
- Hyperlink Types
Program Outcome
Upon successful completion of the course, students will:
- Become acquainted with different statistical methodologies for analyzing 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 analyze 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.