Introductory description

EC306-15 Econometrics 2: Time Series

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Illustrative topics might include:

1. A review of relevant matrix algebra, and maximum likelihood estimation
2. The rationale for dynamic models, and some simple time-series model
3. Unit roots and testing for unit roots
4. Time-series models, model selection and forecasting
5. Spurious regression versus cointegration
6. Multivariate models and cointegration
7. Modelling second moments: ARCH and GARCH models
8. State Space Models

Learning outcomes

By the end of the module, students should be able to:

• Carry out empirical analyses using economic and financial time series data.The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Examination and assessment
• Interpret the results of such analyses, in terms of the validity of the inferences that can be drawn, and to appreciate the interplay between data and theory in making such inferences. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading The summative assessment methods that measure the achievement of this learning outcome are: Examination and assessment
• Which will include demonstration of an ability to critically assess empirical papers. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars and background reading

Indicative reading list

Please see Talis Aspire link for most up to date list.

View reading list on Talis Aspire

Subject specific skills

Students will have the opportunity to develop skills in:
Analysis of time-series models
Analysis of time-series data
Estimation, inference and prediction using time-series data
Relevance of stationarity assumptions and tests for these in the data
Aspects of endogeneity, simultaneity, causality and policy analysis using time-series data
Analysis of multivariate cointegrated data; exposure to modern methods in time-series econometrics.

Transferable skills

Students will have the opportunity to develop:
Research skills
Numeracy and quantitative skills
Data-based skills
IT skills
Written communication skills
Mathematical, statistical and data-based research skills