Applied Econometrics

About the course

Subjects

Microeconomics Macroeconomics International economics

Course content

Some of the main tools of econometrics that are standard when analyzing data from fields such as industrial organization, economics and finance at the graduate level are introduced. Emphasis will be on analysis of economic data by means of statistical models, i.e. regression models and time series models. Some relevant software will be used in handling data introduced for computer based exercises and illustrations. Integration with the other courses at the line will take place via the selection of material for illustrations and exercises.
Some of the topics covered are:
The simple and multiple regression models and their interpretation.
Basic time series models (ARIMA, distributed lag models, VAR), estimation, testing and forecasting.

More advanced topics (e.g. ARCH) only at the introductory level.

See course description in course catalogue

What you will learn

The skills developed are valuable and necessary for handling of data and understanding empirical results related to most of the other courses of the concentration. After this course it is the aim that the students can:

Explain the concepts, models, methods and tools of econometrics as discussed during the course.
Perform an academically founded econometric analysis on an identified problem in practice.
Select, estimate and apply a suitable econometric model, including the assumptions, of the model toward solving a problem founded in practise.
Discuss and solve any important problems encountered in relation to the analysis (e.g. heteroscedasticity for a cross-sectional data set)
Perform hypotheses testing of both simple and more composite hypotheses
Apply the model for the purpose it was constructed for (e.g. testing a theory or making forecasts).
Report the results and conclusion of the analysis both if the reader of the report is an econometrician and if the reader is a non-econometrician (e.g. the CEO).
Evaluate an empirical study conducted by another person/researcher and read and interpret output from statistical computer software

Facts

Level

Master

Type

Mandatory course

ECTS

7.5

Code

CEAPO1004U

Teaching periods

Autumn – semester

Academic year

2024/2025

Status

Available places

Teaching language

English

Teaching Format

Hybrid

Exams

Written exam on campus
Individual exam, winter

Assessment type

7 point grading scale