MSB104 Econometrics

Contents and structure

MSB104 Econometrics is the compulsory course at the master's level in econometrics. It builds upon the bachelor course in statistics and prepares the student for further studies in advanced econometrics. This course provides the student with a broad knowledge of statistical methods for analyzing simple and multivariate econometric models, as well as a deep understanding of the regression model estimated, using the method of least squares, and its properties. The methods are primarily used on cross-sectional and time series data, but students are also introduced to panel data. In addition to the theoretical approach, students learn to use statistical software to perform their own analysis, using the free cross-platform package R from the IDE RStudio.

Finally, the students will learn how to draw valid inference using sound statistical thinking.

Learning Outcome

Learning outcomes

Knowledge

· a deep knowledge of fundamental least squares regression analysis applied in economics and its properties.

· a strong critical awareness of potential mis-interpretations of of the outcomes of empirical analysis (e.g. causation vs. correlation), as well as a good understanding of the basic challenges related to the various methods and datastructures.

· a good knowledge of the elementary procedures for model validation.Problems related to heteroscedasticity, autocorrelation, model specification, multicollinarity and non-stationarity.

· Theoretical background for the standard methods used in empirical analyses for statistical testing of hypothesis.

Skills

· Carry out a basic empirical analysis on a given dataset, formulate basic econometric models, estimate parameteres, make predictions, test relevant hypotheses, and how to draw sound inference from the results

· basic use of the statistical package "R" for econometric analyses.

· perform statistical tests to investigate whether the classical assumptions in regression analysis are satisfied.

· be a critical reader of other empirical analyses.

Competance

· be able to read and understand project reports and journal articles that make use of fundamental statistical concepts and methods

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

· Develop a cricitical attitude to statistical analysis

Entry requirements

None

Recommended previous knowledge

A basic course in statistic analysis and scientific method is strongly recommended. It is also strongly recommended that you passed the Data Science course or take part in it in the same semester.

Teaching methods

Lectures, data-workshops and assignments.

Compulsory learning activities

Learning portfolio.

The students will build a portfolio based on the written assignments that are given during the course.

Assessment

Portfolio.

The students will build a portfolio based on the written assignments that are given during the course. The portfolio will be assessed on a scale A-F.

The tasks must be written in Norwegian unless otherwise agreed upon with the course instructor.

Examination support material

All study aids allowed. The students shall adhere to normal scientific citation practice, and portfolio documents will be checked for plagiarism. The use of AI in solving the tasks must be documented.

More about examination support material