Facts about the course

ECTS Credits:
Responsible department:
Faculty of Business Administration and Social Sciences
Course Leader:
Andrew Muteti Musau
Lecture Semester:
Spring, Autumn
Teaching language:
½ year

BØK730 Econometrics (Autumn 2022)

About the course

This course provides a broad overview of statistical methods for analyzing single equation econometrics models. Apart from presenting the theory behind the models, the course emphasizes a hands-on approach to econometric analysis, from preparation of the data to post-estimation.  The statistical package Stata will be used. Topics include, but are not limited to:

  • Multiple regression analysis - theory and practical aspects

  • Topics in multiple regression - categorical data, nonlinear effects, deviations from standard assumptions: specification bias, heteroscedasticity, multicollinearity, autocorrelation

  • Instrumental variables regression

  • Binary choice and limited dependent variable models

  • Time series analysis - decomposition, moving averages, exponential smoothing, autoregressive models

  • Panel data models for continuous, binary and ordered responses

The course is connected to the following study programs

Recommended requirements

Background knowledge in statistics 

The student's learning outcomes after completing the course

Learning outcome


After completing the course, students should have skills and knowledge in statistical methodology constituting a base from which they can successfully carry out solid empirical work, e.g., in their master thesis or later in their academic or professional careers. Specifically, the students should be able to

  • confidently perform estimation and testing of hypotheses about main population parameters such as means, proportions, variances

  • specify and estimate linear regression models, using appropriate theory and sample data

  • identify and handle nonlinear effects in regression models using transformations and dummy variables

  • identify and handle heteroscedasticity, multicollinearity and autocorrelation in regression data

  • employ binary response and limited dependent variable models in cases where the outcome is not continuous

  • work with basic time series models and do forecasting with moving averages, exponential smoothing

  • use panel data methods for continuous, binary and ordered responses

  • interpret the result of statistical analyses and explain the results in nontechnical language

It is expected that the students will:

  • be able to apply modern computer programs in regression analyses of empirical data, including statistical testing to investigate whether the classical assumptions in regression analysis are satisfied

  • be a qualified user of basic econometric methods

  • perform be a critical reader of the literature concerning empirical analyses

  • be able to perform statistical analysis using the statistical package Stata

Furthermore, the students should:

  • be able to read and understand project reports and journal articles that make use of the concepts and methods that are introduced in the course

  • be able to make use of econometric models in their own academic work, for example in analyses needed for their master’s thesis


Forms of teaching and learning

Classroom lectures and lab sessions held in the computer lab. 2 two-hour sessions per week.  

Coursework requirements - conditions for taking the exam

  • Obligatorisk arbeidskrav: Oppgave(r)

  • Antall arbeidskrav: 2

  • Påkrevde arbeidskrav: 2

  • Fremmøte: Ikke påkrevd

  • Kommentar: There will be two mandatory assignments. Both need to be passed to take the final exam.


  • Vurderingsform: Digital skoleeksamen - Inspera

  • Andel: 100%

  • Varighet: 4 timer

  • Gruppering: Individuell

  • Karakterskala: Bokstavkarakter (A - F)

  • Hjelpemidler:




Last updated from FS (Common Student System) Dec. 6, 2022 8:20:38 AM