Module ECON41615: TIME SERIES ANALYSIS

Department: Economics

ECON41615: TIME SERIES ANALYSIS

Prerequisites

• None

Corequisites

• Econometric Analysis (ECON41515)

Excluded Combination of Modules

• None

Aims

• to build upon the knowledge gained in Econometric Analysis and provide students with the specific advanced technical skills (both theoretical and practical) necessary to understand the methods employed by macro-econometricians and financial econometricians;
• to provide students with the tools required to model stationary and non-stationary time series data and obtain forecasts from econometric models.

Content

• Topics are likely to include:
• Time series data, stationarity, ARMA models, Box-Jenkins methodology
• Forecasting
• Models for non-stationary data, Unit root tests
• Cointegration: Single-equation methods, Engle-Granger methodology, Error correction model (ECM)
• Dynamic regression models, Distributed lag models and Autoregressive distributed lag models
• VAR models, Impulse Response Analysis
• Cointegration in a System: VECM, Johansen approach
• Volatility Models: ARCH, GARCH

Learning Outcomes

• have an advanced knowledge of the principles and methods of modern macroeconometrics and financial econometrics;
• have extended and deepened their understanding of econometrics gained in Econometric Analysis, and improved their critical judgement and discrimination in the choice of techniques applicable to complex situations;
• have extended their understanding of the application of econometric methods and interpretation of the results at an advanced level;
• have extended their understanding of the use of econometric tools to conduct advanced empirical investigations into complex specialised issues.

• have further practised problem solving skills in econometrics at an advanced level and the use of econometric software.

• Written Communication;
• Planning, Organising and Time Management;
• Problem Solving and Analysis;
• Using Initiative;
• Numeracy;
• Computer Literacy.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• A combination of lectures, seminars, computer classes and guided reading will contribute to achieving the aims and learning outcomes of this module. The summative assignment and examination will test students knowledge and critical understanding of the material covered in the module, their analytical and problem-solving skills.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

One formative assessment to prepare students for the summative exam.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University