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Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title Applied Econometrics:Sustainable Eng. Markets
Module Code EF5112
School DCUBS
Online Module Resources

Module Co-ordinatorSemester 1: Mark Cummins
Semester 2: Mark Cummins
Autumn: Mark Cummins
Module TeacherMark Cummins
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Description
This module is an introductory econometrics module where participants with a basic knowledge of statistical inference learn how to use standard econometric methods for market analysis pertinent to the sustainable energy sector. The approach taken reflects the needs of participants (i) as informed consumers of econometric information and work that uses econometric techniques; and (ii) as users of econometric methods to test or explore economic and financial hypotheses and modelling. Additionally, this modules provides a platform of knowledge that allows participants to tackle work that requires more sophisticated econometric approaches. Data sets and examples are drawn from sustainable energy markets.

Learning Outcomes
1. Use linear regression methods to estimate empirical relationships.
2. Evaluate and mitigate the effects of departures from classical statistical assumptions on linear regression estimates.
3. Understand the statistical features of sustainable energy markets, with particular focus on the emissions markets.
4. Critically evaluate econometric analyses of sustainable energy markets.
5. Design and implement an empirical investigation of the sustainable energy markets.



Workload Full-time hours per semester
Type Hours Description
Lecture24Econometric methods: outline and explanation.
Lecturer-supervised learning (contact)12Exercises involving review of practical empirical work.
Laboratory12Computer lab sessions.
On-line learning18Moodle: weekly exercises and quizzes.
Assignment3Project: desk work, lab work, and write-up.
Assignment12Empirical project: desk work, lab work, and write-up.
Independent learning time44Weekly review of class materials; preparation for class tests; preparation for final examination.
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities
Learning Activities.
Each topic will be first introduced in a formal lecture setting. Thereafter, students will be shown relevant empirical applications from sustainable energy and emissions markets - both lecturer-generated and from the academic literature - and invited to critically review this work, both individually and in small groups. Further learning will take place in practical estimation exercises run in the computer labs: students will use a standard econometric 'package', e.g., GRETL, to produce estimates of econometric relationships and will then provide a critical review of same..

Introduction.
Data types – time series, cross-sectional and panel; the classical linear regression model (CLRM) - the least squares principle; estimates and estimators: criteria for estimators - consistency, unbiasedness, and efficiency; univariate to multivariate regression; hypothesis testing – t-tests and F-tests; type I and II errors; price drivers in emissions markets..

Further Topics in Classical Regression.
Goodness of fit statistics; R2 and adjusted R2; CLRM assumption violations; heterscedasticity – detection, consequences and solution; autocorrelation – patterns of serieal correlation, detection, consequences and solution; multicollinearity – definition and measurement; residual normality - detection, consequences and solution; parameter stability and structural breaks; structural breaks in emissions prices..

Time Series Analysis.
Stationarity; moving average series; autoregressive series; ARMA and ARIMA models; forecasting in sustainable energy markets; simultaneous equation models; vector autoregression (VAR); VAR analysis of EUA and CER emissions markets; Granger causality; emissions and energy market interactions..

Cointegration and Volatility Modelling.
Order of integratedness; the spurious regression problem; stationarity; testing for unit roots; cointegration; Engle-Granger test; Johansen methodology; statistical arbitrage; arbitrage in the EUA-CER spread; ARCH/GARCH modelling; GARCH modelling of emissions prices..

Assessment Breakdown
Continuous Assessment50% Examination Weight50%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class TestWritten examination5%Week 3
AssignmentIndividual project15%Week 6
In Class TestWritten examination5%Week 7
Group project Group project20%Week 9
In Class TestWritten examination5%Week 10
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category 1
Indicative Reading List
  • C. Brooks: 2008, Introductory Econometrics for Finance, 2nd, Cambridge University Press,
  • J. Chevallier: 2011, Econometric Analysis of Carbon Markets: The European Union Emissions Trading Scheme and the Clean Development Mechanism, Springer,
  • P. Kennedy: 2003, A Guide to Econometrics, 6th, Wiley,
  • A. Serletis: 2007, Quantitative and Empirical Analysis of Energy Markets, Volume 1, World Scientific Publishing,
Other Resources
6947, Journal, 0, Energy Economics, 6948, Journal, 0, The Energy Journal, 6949, Journal, 0, The Journal of Energy Markets, 6950, Journal, 0, Energy Policy, 6951, Journal, 0, Ecological Economics,
Array
Programme or List of Programmes
MSEFMSc in Sustainable Energy Finance
Timetable this semester: Timetable for EF5112
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