LEARNING OUTCOMES
Students are able to
- utilize panel data with an understanding of the benefits
- employ Difference-in-Difference and Regression Discontinuity methods for causal analysis
- model limited dependent variable and describe at a conceptual level how maximum likelihood estimation works
- discuss the connection between econometric models and the basic machine learning models
- classify and estimate time series econometrics, including forecasting models
- describe VAR (Vector AutoRegressive) models and their use
- explain what cointegration is
- describe (G)ARCH (Generalized AutoRegressive Conditional Heteroskedasticity) models and their use
- apply the econometric tools taught in this course and in Econometrics I and use them to perform and present an econometric analysis in the Capstone project
Credits: 5
Schedule: 24.02.2025 - 07.04.2025
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Otto Toivanen
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: Finnish, English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Panel data methods, difference in difference analysis, regression discontinuity analysis and time series analysis. Capstone work.
Assessment Methods and Criteria
valid for whole curriculum period:
Exam, exercises
Workload
valid for whole curriculum period:
Lectures, exercises and preparation
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
8 Decent Work and Economic Growth
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Spring IV
2025-2026 Spring IV