This module presents a systematic and operational approach to the econometric modelling of economic time series, which gives an understanding of the techniques in practical, appropriate, analytical and rigorous manner. Econometric analysis is a core skill in modern economics.
The module links theory to empirical studies of the macroeconomy and includes the following topics:
Univariate Time Series Analysis
• Concepts of stochastic processes;
• Types of linear processes: Autoregressions and moving averages
• Nonstationary linear processes
• Predicting stochastic processes
• Estimation of linear time series models
Dynamic Econometric Models
• Nonsense Regressions;
• The autoregressive distributed lag model;
• Cointegration and equilibrium correction.
Multiple Time Series Models
• Vector autoregressive processes;
• Structural analysis: Causality and impulse-response analysis.
These topics are illustrated with a range of theoretical and applied exercises, which will be discussed in seminars and computer classes. As such, the module emphasises the development of practical skills in the use of software for empirical research, and introduces students to the research methods used by macroeconomists in academia, government departments, think tanks and financial institutions. It also helps students to prepare for the quantitative requirements of a master programme in economics.
Total contact hours: 39 hours
Private study hours: 111
Total study hours: 150
This module is compulsory for Single Honours Economics with Econometrics and Financial Economics with Econometrics.
This module is optional for all other Single and Joint Honours degree programmes in Economics.
This module is available to well-qualified students from other divisions.
Method of assessment
In Course Test (1 hour) (10%)
Group Project (10 pages) (20%)
Examination, 2 hours (70%)
Reassessment Instrument: 100% exam
Time series econometrics is an expansive area of econometric theory and application. Most modern introductory texts provide an introduction to the issues discussed in the module:
* Green, W.H. (2003). Econometric Analysis. 5th edition, Englewood Cliffs, NJ: Prentice
* Gujarati D N (2002). Basic Econometrics. 4th edition, New York: McGraw-Hill.
* Johnston, J. and J. DiNardo (1997). Econometric Methods. 4th edition, New York: McGraw.
* Wooldridge J.M. (2016). Introductory Econometrics. 6th edition, Cengage.
Advanced textbooks on time-series econometrics include:
* Enders, W. (2014), Applied Economics Time Series. 4th edition. New York: Wiley.
* Franses, P.H., vanDijk, D., and A. Opschoor (2014), Time Series Models for Business and Economic Forecasting. 2nd edition. Cambridge: Cambridge University Press.
* Hamilton, J.D. (1994). Time Series Analysis. Princeton: Princeton University Press.
* Hendry, D.F. (1995). Dynamic Econometrics. Oxford: Oxford University Press.
* Lütkepohl H. (2006). Introduction to Multiple Time Series Analysis. New York: Springer.
Additional readings will be given for the selected topics in the module outline.
See the library reading list for this module (Canterbury)
On successfully completing the module students will be able to:
8.1. Understand and abstract the time-series properties of economic data
8.2. Synthesise and critically compare different econometric analyses of an economic issue
8.3. Demonstrate analytical skills that can be used to formulate and consider a range of econometric problems and issues
8.4. Practise the use of econometric concepts especially in relation to time series analysis.
8.5. Demonstrate critical understanding of statistical, graphical and numerical data analyses
8.6. Collate, examine and interpret time-series data in the context of economic theory and policy
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Credit level 6. Higher level module usually taken in Stage 3 of an undergraduate degree.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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