Subtitle spring 2018: "Machine Learning and Econometrics"
This course will provide an overview of econometric methods appropriate for the analysis of social and economic networks. Many social and economic acitivies are embedded in networks. Furthermore, datasets with natural graph theoretic (i.e., network) structure are increasingly available to researchers. We will review (i) how to describle, summarize and visually present network data and (ii) formal econometric models of network formation that admit heterogeneity, strategic behavior, and/or dynamics. The focus will be on the formal development of methods, but selected empirical examples will also be covered, as will methods of practical computation.
This course is offered to PhD candidates at the Department of Economics. Other candidates admitted to a PhD program may apply.
The course takes place during one intensive week.
Credit for the course requires both active participation in class and a take home exam.
Take home exam
Language of examination
The examination text is given in English, and you submit your response in English.
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
It is recommended to request an explanation of your grade before you decide to appeal.
The deadline to request an explanation is one week after the grade is published. For oral and practical examinations, the deadline is immediately after you have received your grade.
The explanation should normally be given within two weeks after you have asked for it. The examiner decides whether the explanation is to be given in writing or verbally.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.