Econometrics

ECN402 Econometrics

Autumn 2020

  • 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

    Upon completion of the course, students will

    Knowledge

    • 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

    Skills

    • 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

    General competences

    • 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
    • understand the ethical issues in collecting, storing and using data
    • have a good background for more advanced econometric courses

  • Teaching

    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 receive assistance in solving assignments. 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.

  • 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 can not be combined with BUS444, BUS444E, BAN431, FIE401/FIE401A/FIE401B or FIE449, due to similarities - and students 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.

  • Assessment

    One final written school exam of four (4) hours. The exam answer must be in English.

  • Grading Scale

    Grading scale A - F.

  • Computer tools

    Econometric software package STATA.

  • 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

Overview

ECTS Credits
7.5
Teaching language
English.
Semester

Autumn and spring.  Offered Autumn 2020.

Please note: Due to the present corona situation, please expect parts of this course description to be changed before the autumn semester starts. Particularly, but not exclusively, this relates to teaching methods, mandatory requirements and assessment.

Course responsible

Professor Øivind Anti Nilsen, Department of Economics

Professor Astrid Kunze, Department of Economics.