Entry requirements

At least 52.5 credits from Economics A and B and 15 credits in statistics or mathematics.

Learning outcomes

After completing the course, the student is expected to be able to

- independently plan, perform, and present basic empirical work within economics,

- explain and interpret results from regression analyses,

- explain and understand the potentials and limitations of regression analysis,

- understand and explain the intuition behind the most common statistical methods used to isolate causal effects,

- identify strengths and weaknesses in empirical studies,

- perform basic data processing and estimation with the statistical software Stata.

Content

The course will cover tools and concepts that are widely used in economic and many other social science studies as well as within a large number of professional areas such as public administration, financial analysis, and marketing. The course focuses on the analysis of cross-sectional data and the concepts, intuition and software skills needed to independently perform and interpret results from a multiple regression analysis. The statistical methods and concepts covered include multiple regression analysis with continuous, dummy, and interaction variables, t- and F-tests, and omitted variable bias. The course also introduces students to the intuition behind more advanced statistical methods commonly used in economics to isolate casual effects, such as instrumental variable methods. The discussed methods are applied to real world data with the use of the statistical software Stata.

Instruction

The course consists of lectures and applied work in the computer lab

Assessment

The course will be examined through independent analyses of several take-home assignments. There will be three grades: pass with distinction (VG), pass (G) and fail (U).