Course description

The course begins with a block of preparatory and statistics. This part of the course covers probability theory, such as random variables, probability distribution, expected values, independent samples, the central limit theorem, the law of large numbers, and consistency.       

Estimation of the population mean, hypothesis testing, and confidence intervals are also discussed. The course then discusses the linear regression model under its classical assumptions (OLS), as well as hypothesis tests and confidence intervals for both simple and multiple regressions. Different types of typical problems for the linear model, such as non-linear functions, omitted and irrelevant variables, simultaneity, measurement errors, heteroscedasticity, and autocorrelation are analysed. In addition, the course includes an overview of time series data, including autocorrelation, non-stationarity, forecasting, structural breaks, and unit roots.

Lecturer and Examiner:
Peter Skogman-Thoursie, Office hours: by agreement
Phone: 08 163048, e-mail:

Sergio de Ferra, Office hours: by agreement, room A714,

Group Teachers:
Fabian Sinn, Office hours: by agreement, room A959,

Monir Bounadi, Office hours: by agreement, room A409,

Veronika Wallinder. Office hours: Mon-Thu, 12.30-13.30, room A491,
phone 08 16 31 12, e-mail:

Course information