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

Activity no.: 5246-20-03-31 
Enrollment deadline: 08/08/2022
PlaceA2-84.12
Thorvaldsensvej 40
Date and time15.08.2022, at: 09:00 - 19.08.2022, at: 16:30
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
Social science researchers are usually interested in investigating causal relationships. While the analysis of
causal relationships is easiest when using experimental data, social-science experiments are not always
feasible, and when they are feasible, they may suffer from important limitations. As a result, most empirical
studies in the social sciences are based on observational (i.e., nonexperimental) data.

Participants in this course will learn state-of-the-art empirical methods used for investigating causal
relationships with observational data. Course participants will also learn how to evaluate and discuss the
appropriateness of identification strategies for analysing causal relationships, and they will learn to choose the
most appropriate identification strategy for analysing a specific research question. All this will help participants
obtain more credible and reliable results in their empirical work, and to publish their work in better journals.

This course will focus on identification strategies in econometric analyses for (a) estimating the effects of
weather / climate (change) and (b) assessing the impacts of intervention programs.

Learning outcome
After having completed the course, the course participants will know the state-of-the-art empirical methods for
causal inference with observational data, and they will be able to apply these methods to investigate causal
relationships based on observational data. 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). All participants should
prepare a concept note (max 1 page, single-spaced, font size 12), in which they explain the identification
strategy that they will use in their exam papers (see below). The participants will present and discuss their
concept notes / identification strategies on the last day of the course, where the exact format of the
presentations and discussions will depend on the number of course participants.

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 at least 2 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 (max ten pages, double-spaced,
font size 11) 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 (1) state their research question, describe (2) their data, (3) their methodology, (4) their model
specification and (5) their (preliminary) results, (6) discuss the appropriateness of their identification strategy,
and (7) 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
Fabio G. Santeramo, Associate Professor, Department of Agricultural, Food, Natural Resources and Engineering,
University of Foggia, Italy
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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