Course content
Upon successfully finishing this course, students will acquire the expertise to meticulously clean, visually represent, and analyze time series data crucial in the realms of business, economics, and finance. This journey of learning encompasses mastering various data collection methods, including adeptly extracting information from online sources. Emphasizing the widely utilized R software, prevalent in financial institutions, firms, and academia, students will become adept at data manipulation—empowering them with a coveted skill set.
The curriculum delves into time series methodologies, enabling students to construct models and make insightful forecasts. Armed with these skills, students will find themselves well-equipped for roles in analytics departments across industries or poised for advanced academic pursuits in fields such as economics, finance, marketing, and related disciplines (see Nordic Nice #3: You recognize humanity’s challenges and have the entrepreneurial knowledge to help resolve them). This course serves as a springboard, propelling students toward a future where they can harness the power of data to drive informed decision-making in diverse professional settings (see Nordic Nine #4: You are competitive in business and compassionate in society).
Topics:
- Autoregressive Integrated Moving Average (ARIMA) model.
- Seasonal Autoregressive Integrated Moving Average (SARIMA) model.
- Modelling structural breaks.
- Autoregressive Distributed Lag (ADL) model.
- Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model.
- Vector Autoregressive (VAR) model.
- Structural Vector Autoregressive (SVAR) model.
- Error Correction Model (ECM).
See course description in course catalogue