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Advanced Applied Econometrics: Recent Methods and Issues
Provider: Faculty of Science

Activity no.: 5246-20-03-31There are no available seats 
Enrollment deadline: 24/05/2020
PlaceA2-70.02
Thorvaldsensvej 40, 1878 Frederiksberg C
Date and timeJune 2020
Regular seats40
Course fee1,000.00 kr.
ECTS credits5.00
Contact personCharlotte Bukdahl Jacobsen    E-mail address: cja@ifro.ku.dk
Enrolment Handling/Course OrganiserArne Henningsen    E-mail address: arne@ifro.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockSpring
Exam formWritten assignment
Exam aidsAll aids allowed
Grading scalePassed / Not passed
Course workload
Course workload categoryHours
Lectures25.00
Exercises9.00
Course Preparation40.00
Evaluation1.00
Exam60.00

Sum135.00


Aim and content
****THE COURSE IS POSTPONED UNtiL AFTER THE COVID 19 CRISIS******

Social science researchers are usually interested in investigating causal relationships. The analysis of causal relationships is generally easiest based on experimental data. The use of experiments in social sciences, however, has many limitations, and most empirical studies are based on observational (i.e., nonexperimental) data. The participants of this course will learn the theory and practice of state-of-the-art empirical approaches used for investigating causal relationships with observational data. The course participants will also learn how to evaluate and discuss the appropriateness of identification strategies for analysing causal relationships and to choose the most appropriate identification strategy for analysing a specific research question. All this will help the participants obtain more credible and reliable results in their empirical work and to publish their work in better journals. This course will have a similar general topic as the course “Advanced Applied Econometrics: Causal Inference with Observational Data” that the same two teachers taught in May 2018 but this new course will cover several new methodologies and innovations in applied econometrics, such as the use of directed acyclic graphs for identification, back-door adjustments, front-door criterion estimation, synthetic control methods, randomization inference, and so on.

Learning outcome
After having completed the course, the course participants will know state-of-the-art statistical and econometric methods for causal inference with observational data, and they will be able to apply these methods to investigate causal relationships based on observational data. The participants will also be able to evaluate and discuss the appropriateness of identification strategies and they will be able to choose an appropriate identification strategy for a specific research question.

Teaching and learning methods
The theory will be taught mostly through (interactive) lectures, active learning, dialogue teaching, and self-study. The practice will be taught mostly through exercises, group work, self-study, and the exam (i.e., conducting econometric/statistical analyses and writing a report, see following section).

Preparation and self-study:
It is expected that the participants prepare for the course by reading the course material (mostly extracts from textbooks and journal articles) that will be sent to the students about 4 weeks before the course starts. It is also expected that the participants recapitulate the contents of the course by reviewing the lecture notes and other course material in order to appropriately conduct the analyses and write their report for the exam.

Exam form and criteria for assessment:
The exam consists of a brief report about a statistical or econometric analysis (maximum ten pages double-spaced) that each participant has to write and send to the course organiser no later than two months after the end of the course. The participants need to find a suitable research question and data set for their analyses themselves. The research question needs to be within the social sciences and needs to address a causal relationship. The data set needs to contain observational (i.e., nonexperimental) data. It is encouraged that the research question and data set are parts of the participants’ PhD projects. In their reports, the participants need to state their research question, describe their data, methodology, model specification and results, discuss the appropriateness of their identification strategy, and derive conclusions based on their empirical analysis. A participant passes the exam if his/her report indicates that he/she has obtained the intended learning outcome (see section “learning outcome”).

Lecturers
Marc F. Bellemare, Northrop Professor, Department of Applied Economics, University of Minnesota, USA
Arne Henningsen, Associate Professor, Department of Food and Resource Economics, University of Copenhagen, Denmark

Remarks
Practicalities:
Coffee, tea, and lunch will be provided at each of the five teaching days. Dinner will be provided at one of the teaching days. Participants need to find accommodation themselves. Teaching will take place Monday to Thursday 9:00-12:00 and 13:00-17:00 as well as Friday 9:00-12:00 and 13:00-16:30.

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