Content, progress and pedagogy of the module

Disclaimer.
This is an English translation of the module. In case of discrepancy between the translation and the Danish version, the Danish version of the module is valid.

Learning objectives

Knowledge

• knows about conditioning in the multidimensional normal distribution as well as the usual and generalized least squares method and the resulting OLS and GLS estimators
• can understand time series analysis as a stochastic process and understand the connection between stochastic processes and dynamic systems and knows about the stochastic processes known as the Box-Jenkins models, including especially the ARMA models
• Exponential smoothing
• Forecasting methods for time series

Skills

• is able to theoretically interpret the statistical properties of time series models
• can carry out all the phases of a classic time series analysis: Identification, estimation, model control, forecasting and statistical interpretation
• can use correlograms and other graphic aids in the identification phase
• can use and become familiar with newer statistical methods for analyzing time series

Competences

• is able to apply the concepts of time series analysis to a concrete problem
• can make estimation and forecasting in practice using statistical software

Type of instruction

The type of instruction is organised in accordance with the general instruction methods of the programme, cf. § 17.

Extent and expected workload

The student is expected to spend 30 hours per ECTS, which for this activity means 150 hours.

Exam

Exams