The course introduces regression analysis applied to cross-sectional data, panel data and time-series data. Instrumental variables and differences-in-differences techniques to solve potential endogeneity problems will also be taught. The course will focus on applications of the econometric techniques and on practical and empirical examples.
- the simple regression model, and regression with multiple regressors
- potential outcomes, causality and correlations
- panel data techniques and differences-in-differences
- time-series analysis
- instrumental variable techniques
After completing this course, students should:
- understand what assumptions econometric models are based on
- understand the necessary assumptions to interpret our estimates as effects relevant for policy and decision making
- know the central concepts and terminology of econometrics
- be able to interpret the results of empirical analyses
- be able to choose between regression models, appropriate control variables and potentially important non-linearities and functional forms
- be able to assess the validity of causal claims, and to disentangle correlations and causality
- be able to conduct quantitative analysis where several factors can affect an outcome variable simultaneously
- be able to use STATA for doing econometric analysis, produce do-files and log-files, import data in different formats, and produce tables and figures
- be able to choose and apply an appropriate scientific method for analysing the research question
- be able to interpret and critically deal with empirical work in applied econometrics
- know the structure and requirements for a master thesis, and be able to develop a research question
- understand the ethical issues in collecting, storing and using data
- have a good background for more advanced econometric courses
15 lectures/classes and 5 practical computer sessions where the students learn the use of the econometric software STATA. The first computer session introduces STATA, and in the 4 remaining sessions the students will get help in solving an assignment. Students need to bring their own computer. Two of the four assignments must be submitted in order to get course approval. Assignments may be submitted, in groups of 3-4 students, and some feedback on the assignments will be given. Assignments, must be written in English.
Basic knowledge about statistics.
Credit reduction due to overlap
ECN402 is a renaming of the previous ECO402, and you will not get credit for both courses.
ECN402 can not be combined with BUS444, BUS444E, BAN431, FIE401/FIE401A/FIE401B or FIE449, due to similarities - and you will not get credit for both courses.
Requirements for course approval
Two of four assignments must be submitted and approved in order to get course approval. Assignments may be submitted, in groups of 3-4 students, and some feedback on the assignments will be given. Assignments must be written in English.
One final written school exam of four (4) hours. The exam answer must be in English.
Grading scale A - F.
Econometric software package STATA.
Jeffrey M. Wooldridge (2019): Introductory Econometrics: A Modern Approach, 7th edition
Joshua D. Angrist and Jörn-Steffen Pischke (2014): Mastering ’Metrics: The Path from Cause to Effect.
Some additional material will be distributed on the learning platform
- ECTS Credits
- Teaching language
Autumn and spring. Offered spring 2020.
Assistant professor Morten Sæthre, Department of Economics and Assistant professor Vincent Somville, Department of Economics.