ECN402 Econometrics

• Topics

  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.

• Learning outcome

  Knowledge

  Upon completion of the course, the student can…

  • recognise the assumptions econometric models are based on
  • identify the necessary assumptions to interpret our estimates as effects relevant for policy and decision making
  • describe the central concepts and terminology of econometrics

  Skills

  Upon completion of the course, the student can…

  • interpret the results of empirical analyses
  • choose between regression models, appropriate control variables and potentially important non-linearities and functional forms
  • assess the validity of causal claims, and to disentangle correlations and causality
  • conduct quantitative analysis where several factors can affect an outcome variable simultaneously
  • use STATA or R for doing reproducible econometric analysis, import data in different formats, and produce tables and figures
  • choose and apply an appropriate scientific method for analysing the research question

  General competence

  Upon completion of the course, the student can…

  • interpret and critically assess empirical work in applied econometrics
  • recognise the structure and requirements for a master thesis, and be able to develop a research question
  • reflect on the ethical issues in collecting, storing and using data
  • demonstrate good background knowledge for more advanced econometric courses

• Teaching

  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. 

• Recommended prerequisites

  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.

• Compulsory Activity

  Three of four assignments must be submitted and approved. Assignments are submitted in groups of no more than 3 students. Feedback will be given on the assignments. Assignments must be written in English.

  Previous course approval is still valid. Note that the final grade is based on one of the four assignments for the current semester (see more information under "Assessment").

• Assessment

  The final grade is based on a submission towards the end of the semester of one self-chosen assignment out of the four assignments you have worked on during the semester (25%) and an individual digital school exam of 3 hours (75%). The assignment and exam must be answered in English. The assignment is submitted in groups.

• Grading Scale

  A-F.

• Computer tools

  R/RStudio.

• Literature

  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 (Leganto).

• Permitted Support Material

  One bilingual dictionary (Category I) 

  Calculator

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support materialhttps://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/ and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/