Prerequisite/Recommended prerequisite for participation in the module

The module builds on knowledge obtained by the module Statistical Inference for Linear Models from the Bachelor of Science (BSc) in Engineering (Mathematical Engineering).

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• know about conditioning in the multivariate normal distribution as well as ordinary and generalized least squares methods
• are able to understand a time series as a stochastic process and understand the connection between stochastic processes and dynamical systems, and in particular the Box-Jenkins models (ARMA-type models)
• know about various stationarity and non-stationarity concepts for Time Series: Weak and strong stationarity, causality, autocovariance- and autocorrelation functions, integrated models, long memory models, volatility models, and basic state-space models
• know about various modern time series and econometric models within financial econometrics and financial engineering in discrete time

Skills

• are able to interpret the statistical and possibly econometric properties of time series
• are able to implement all phases in a classical time series analysis: Identification, estimation, diagnostic checking, prediction, and statistical/econometric interpretation
• are able to use correlograms and other graphical tools in the identification phase
• are able to apply and make themselves acquainted with new statistical methods to analyse time series

Competences

• are able to apply the concepts from time series in an econometric or other broader context
• are able to perform qualified econometric analyses of financial and other data including estimation and prediction using available software
• are able to reflect on the discipline's approach to academic problems at a high level and the discipline's relationship to other subject areas
• are able to involve the knowledge area in solving complex problems and thus achieve a new understanding of a given subject area

Type of instruction

As described in §17.

Extent and expected workload

This is a 5 ECTS course module and the work load is expected to be 150 hours for the student.

Exam

Prerequisite for enrollment for the exam

• Only for students on master-level: In order to participate in the course evaluation, students must have actively participated in course progress by way of one or several independent oral and/or written contributions.

Exams

Name of exam	Time Series and Econometrics
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	As stated in the Joint Programme Regulations. http:/​/​www.engineering.aau.dk/​uddannelse/​Studieadministration/​