Course Syllabus
Econometrics Applied:Making Data Talk |
 |
| Semester & Location: |
Spring 2023 - DIS Copenhagen |
| Type & Credits: |
Elective Course - 3 credits |
| Major Disciplines: |
Economics, Politics, Public Health, and more |
| Prerequisite: |
One course in advanced macroeconomics at university level. |
| Faculty Members: |
Mr. Nicola Menale nicola.menale@dis.dk |
| Time & Place: |
Monday and Thursday, 18:00 - 19:20 |
Description of Course
Data is the raw material of the information age, the new gold, as some say. Knowing how to handle and analyze data and to draw the right conclusions is a key qualification not only in a business environment, but for a wide range of professions. This qualification can only grow in importance.
This course is an introduction to a specific kind of data analysis - the science and art of building and using basic econometric models.
Applied econometrics is when theory meets data from the real world in order to answer a research question. It is the application of quantitative methods to explain the relationship (strength and direction) between variables or to forecast future trends. The techniques can be applied to a wide range of questions in economics and beyond, for example:
- What is driving sales of a good – the price or the marketing expenses?
- Do smaller classes improve learning?
- Does democracy increase economic growth?
- Does higher education reduce crime rates?
- On financial markets, econometric models can be used to forecast stock prices
The beauty of econometrics lies in the chance to base the answers to this type of questions on theory and empirical methods instead of gut feeling, prejudice or anecdotal evidence.
The purpose of this course is to give students insight and experience in how to apply basic econometric methods to relevant topics. The course consists of lectures and a large number of smaller and larger exercises both in class and at home. The lectures will lay the necessary theoretical groundwork. Some of the practical exercises can be done in Excel, others with the Econometrics libraries in Python .
The first step will be to develop a feeling for data and data problems. Much of the course will be about regressions, i.e. explaining – in a statistical sense – a dependent variable by one or several independent variables, based on economic (or other) theory. While it is quite easy nowadays to make a computer print an econometric output, interpreting the results carefully is vital for good econometric practice.
The students will learn to build regression models, to interpret the results carefully, to judge the quality of the model and also what to do when the assumptions behind the model are not fulfilled. They will also learn about causality tests and the basics of times series analysis and forecasting. To do this some statistical theory is necessary as a foundation for econometrics.
Learning Objectives
By the end of the course, the students should ...
- have a sense for the power and limitations of the econometric methods studied
- be capable the use Excel and Python for econometric analysis
- be capable to work with the principles of the linear regression model
- be capable to draw the defendable conclusions from large amounts of data
- be capable to do basic time series analysis
- have proven the capability to develop an econometric research project
- have learned to remain humble even when the model looks perfect
- have increased their cross-culture skills
- have increased their collaborative skills.
Faculty
You will have two teachers, both working as external faculty for DIS. Holger Sandte studied Economics and languages in Germany and France. MSc (Economics, University of Hanover) 1993, PhD 1998 with a thesis on whether moderate inflation harms economic growth (answer: mostly not, but it depends). Econometric methods like regressions and VAR-models made up a large part of the empirical section of the thesis. Holger worked for many years as a bank economist Germany and France, then for the Danish Ministry of Finance and between 2019 and 2021 as full-time faculty at DIS. In January 2022, he started working for EKF, Denmark's national export credit agency. He firmly believes that
- ... finding answers to relevant real-world questions by combing theory with empirical work is a truly satisfying experience for students and ...
- ... that students acquire important skills if they learn how to handle data competently.
Nicola Menale studied Business Economics in Vanvitelli University (Italy) in 2011 (with Thesys focused in Game Theory & Microeconomics). MSc in Economics and Social Sciences in Bocconi University (Italy) in 2013 with Thesys focused in Game theory & Decision Making. Master in Data Science at Nicoló Cusano University (Italy) in 2020 with Thesys focused in Deep Neural Network. Since 2013 he is working in Copenhagen in the data analysis field. He worked for International companies as Oticon A/S, Hollister inc., Radiometer A/S and he is currently working in Danske Bank as Senior Business Intelligence Analyst. Nicola is part of the DIS family since September 2019. His motto is "In God we trust, all the rest must bring data".
Readings
... will bring the students in contact with experienced users of econometric tools e.g. in think tanks or consulting firms.
Guest Lecturers ...
... will be invited to widen the students' perspective.
Approach to Teaching
In order to make our classes productive, it is important that you are well prepared. Preparation will usually consist of going through readings/videos and / or practical exercises in Excel and Python. In the calendar and in announcements you will see what there is to do.
Learning econometrics is like learning how to drive car. First you need to learn some vocabulary (some of which sounds more daunting than it really is), and to acquire some theoretical knowledge. Otherwise the risk of accidents is really high. On this foundation it's all about praticing, first on easy roads and then in more and more difficult terrain. As a car driver, you get better over time. More miles/kilometers mean more experience also in unexpected situations and higher skills. Practice makes perfect, as the saying goes.
In the course, we will jump back and forth between theory and practice (with Excel and the Econometrics lybraries in Python). Classes will happen as mix of lectures and exercises of varying complexity, do be done alone or in small groups.
Note that making and analyzing mistakes is a necessary part of the process to improve your skills.
Expectations of the Students
Prerequisites: One course in advanced macroeconomics at university level
The students should be quite familiar with Excel and have basic knowledge in statistics – and no allergy to equations, Greek letters and working a lot with data. Prior knowledge of an econometric software would be helpful but is not required. Active engagement and willingness to learn how to handle data are important.
Evaluation
| Assignment |
Percent |
| Engagement/participation |
20% |
|
Various quizzes/exercises |
50% |
|
Econometric research project |
30% |
Engagement/participation in class: 20%
Your participation grade will be determined by 3 factors: attendance, preparedness for class, and active engagement in class including field studies. You are required to attend all classes. If you miss a class, you must contact an instructor as soon as possible (before the class starts) and provide an explanation. The assigned readings for each lecture should be read prior to the class.
Various quizzes and exercises: 50%
Several quizzes are meant to evaluate your progress during the course. The quizzes refer to the assigned readings and to what we did in class. The answers have to be submitted electronically via Canvas.
Econometric research project: 30%
In your research project towards the end of the semester, you will apply econometric tools to a research question of our choice. The research project can be done alone or in small groups.
Academic Regulations
Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on:
DIS - Study Abroad in Scandinavia - www.DISabroad.org
Course Summary:
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