Spring 2023Autumn 2022
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.
Upon completion of the course, students will
- be able to recognise the assumptions econometric models are based on
- be able to identify the necessary assumptions to interpret our estimates as effects relevant for policy and decision making
- be able to describe 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 or R for doing reproducible econometric analysis, 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 assess empirical work in applied econometrics
- know the structure and requirements for a master thesis, and be able to develop a research question
- be able to reflect on the ethical issues in collecting, storing and using data
- have a good background for more advanced econometric courses
The course consists of 15 lectures/classes and 5 practical computer sessions where the students learn to use the programming language R or the software package STATA. The first computer session introduces R/STATA, and in the 4 remaining sessions the students will receive assistance in solving assignments. Students need to bring their own computer. Three of the four assignments must be submitted in order to fullfill compulsory activities (work requirements) Assignments may be submitted in groups, and some feedback on the assignments will be given. Assignments must be written in English.
Teaching sessions will be held in the auditorium. Sessions featuring in-class participation (e.g. discussions, case studies) will not be filmed. Regular lectures will be recorded. Additional video clips explaining key concepts and calculations will be available.
The 5 practical computer sessions will be offered in the auditorium or smaller group rooms. They are supervised by teaching assistants.
Basic knowledge in statistics.
Credit reduction due to overlap
ECN402 is a renaming of the previous ECO402, and students cannot get credit for both courses.
ECN402 cannot be combined with BUS444, BUS444E, BAN431, FIE401/FIE401A/FIE401B or FIE449, due to similarities - and students will not get credit for both courses.
Three of four assignments must be submitted and approved. Assignments may be submitted in groups of maximum 3 students. Feedback will be given on the assignments. Assignments must be written in English.
The final grade is based on an individual school exam of four (4) hours. The exam answer must be in English.
Econometric software package STATA or RStudio.
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 2023.
Associate Professor Vincent Somville, Department of Economics (main course responsible)
Professor Øivind Anti Nilsen, Department of Economics