Advanced Time Series Econometrics
| Code | School | Level | Credits | Semesters |
| ECON3076 | School of Economics | 3 | 10 | Spring China |
- Code
- ECON3076
- School
- School of Economics
- Level
- 3
- Credits
- 10
- Semesters
- Spring China
Summary
This module is a continuation of the module on time series analysis taken in the second semester of the third year. While the earlier module was devoted to basic time series model building methodology, applicable over a broad range of disciplines, the present module concentrates on those developments which can be applied in the subject of Economics. In particular, the emphasis will be on aspects of the behaviour of typical economic time series, and the implications of that behaviour in practical analysis, such as the construction of models linking economic time series. The key issues addresses will be the identification of non-stationarity through the construction of formal tests and the implications for modelling with non-stationary data. Particular attention will be paid to the contributions of Sir Clive Granger to the spurious regression problem and to cointegration analysis, for which he was ultimately awarded the `Nobel Prize`.
Prerequisite: ECON 2052 Econometric Theory II
Please note this module is assessed at the end of in Spring semester. First sit/ Resit exams are scheduled normally in the summer and can take the same form as the missing/ failed component of the assessment (exam, essay etc.) or other form, as decided by the School.
Target Students
Year 4 Economics students
Classes
- One 2-hour lecture each week for 9 weeks
Assessment
- 100% Exam1 (1-hour-30-minute): 1.5 hour written exam
Assessed by end of spring semester
Educational Aims
The main aims of the module are:Introduction of the concept of unit roots, and the consequences of that concept for the analysis of economic time series - in particular, the importance of cointegration analysis and error-correction models.The introduction of other time series - analytic concepts and tools that have found applicability in Economics. These include multivariate models of stationary and non-stationary time series processes and the analysis of their long run behaviour.Learning Outcomes
Knowledge and Understanding:
A5 Show understanding of relevant mathematical and statistical techniques.
Intellectual Skills:
B1 Work with abstract concepts and in a context of generality.
B3 Be able to evaluate, analyse and present quantitative data.
Professional / Practical Skills:
C2 Understand the sources and content of economic data and evidence, as well as appropriate methods of analysis.
C3 Be familiar with differing approaches to analysing a given economic problem and the history and development of economic ideas.
Transferable Skills:
D1 Apply mathematical, statistical and graphical techniques in an appropriate manner.
Conveners
- Dr Zhenjiang Lin