Registry

Module Specifications

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

Module Title	Applied Econometrics
Module Code	EF501
School	DCUBS
Online Module Resources
Module Co-ordinator	Semester 1: Margaret Lynch Semester 2: Margaret Lynch Autumn: Margaret Lynch
NFQ level	8	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 in an investment economics context. 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. Additionally, this modules provides a platform of knowledge that allows participants to tackle work that requires more sophisticated econometric approaches.

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. Critically evaluate simple econometric analyses
4. Design and implement an empirical investigation of an aspect of financial markets

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	18	Econometric methods: outline and explanation
Lecturer-supervised learning (contact)	12	Exercises involving review of practical empirical work
Laboratory	12	Computer lab sessions
On-line learning	18	Moodle: weekly exercises and quizzes
Assignment	3	Project (week 5): desk work, lab work, and write-up
Assignment	12	Empirical project: desk work, lab work, and write-up
Independent learning time	50	Weekly 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 - 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., EViews to produce estimates of financial relationships and will then provide a critical review of same..

Introduction.
the classical regression model - the least squares principle; estimates and estimators: criteria for estimators - highest R2, unbiasedness, efficiency, MSE; hypothesis testing – t-tests and F-tests.

Further topics in classical regression:.
specification errors - methodologies and principles; tests for misspecification; stepwise regression; linear restrictions on parameters; multicollinearity - consequences, detection, solutions; dummy variables.

Generalised Least Squares:.
non-spherical disturbances - consequences of violation; heteroscedasticity - detection and solutions; autocorrelated disturbances - patterns of serial correlation, tests for serial correlation.

Instrumental variables:.
measurement errors, errors in variables, distributed lags, simultaneous equations.

Time series analysis and regression methods:.
order of integratedness; the spurious regression problem; testing for unit roots.stationarity and cointegration; ARCH/GARCH modelling.

Univariate methods in forecasting:.
overview of estimation and forecasting using ARIMA models.

Assessment Breakdown
Continuous Assessment	50%	Examination Weight	50%

Course Work Breakdown
Type	Description	% of total	Assessment Date
In Class Test	Review of simple econometric techniques and properties of statistical estimators	10%	Week 4
Project	Project: estimate a simple linear regression model, e.g., the Capital Asset Pricing Model	5%	Week 5
In Class Test	Assess and make judgements about empirical statistical work	10%	Week 8
Assignment	Empirical project based on a topic from the current financial or economic literature	25%	Sem 1 End

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

Chris BROOKS: 2008, Introductory Econometrics for Finance, 2nd, Cambridge University Press,
Peter KENNEDY: 2003, A Guide to Econometrics, 5th, Blackwell, 9780262611831

Other Resources

2192, Moodle, 0, 2193, Econometric software: EViews, Stata, etc, 0,

Array

Programme or List of Programmes

MITB

MSc in Investment, Treasury & Banking

PBSSA

PG Exchange(Business School)

PBSSAO

PG Study Abroad(Business School)

Timetable this semester: Timetable for EF501

Date of Last Revision