Postgraduate

Modules

Modules

NBS8187 : Time-Series Econometrics

Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

The module seeks to:
- advance the understanding on using recent advances in econometrics for testing theories in economics and finance
- provide technical skills necessary to pursue a wide range of empirical research in economics and finance
- offer the opportunity to develop some advanced quantitative skills

The module covers a comprehensive set of essential material on time series analysis.

Outline Of Syllabus

Maximum Likelihood Estmation, Univariate Time Series Models, Volatility Models, Multivariate Time Series Models.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture72:0014:00N/A
Guided Independent StudyAssessment preparation and completion132:0032:00N/A
Scheduled Learning And Teaching ActivitiesPractical21:002:00N/A
Guided Independent StudyDirected research and reading125:0025:00N/A
Scheduled Learning And Teaching ActivitiesSmall group teaching21:002:00N/A
Guided Independent StudyIndependent study125:0025:00N/A
Total100:00
Teaching Rationale And Relationship

-Lectures provide the fundamental technical structure of the methods introduced and an overview of the essential empirical economic and fincancial modelling methods
- Seminars provide an opportunity to enhance both theoretical and practical aspects of modelling
- Workshops provide hands-on computer based experience of modelling exercises.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination902A80N/A
Other Assessment
Description Semester When Set Percentage Comment
Written exercise2M20Written Group Project of 2,000
Assessment Rationale And Relationship

Written exam to assess the theoretical and empirical approach to econometrics.

Written project tests students' understanding of econometric methods and demonstrates that students can analyse and interpret real world data.

Reading Lists

Timetable