Module Specifications

Current Academic Year 2019 - 2020
Please note that this information is subject to change.

Module Title Applied Econometrics
Module Code EF501
School DCUBS
Online Module Resources

Module Co-ordinatorDr Mark CumminsOffice NumberQ132
NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
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
Array. 1
Use linear regression methods to estimate empirical relationships. 1D646D11-4AC1-0001-4729-15B090066B80
2. Evaluate and mitigate the effects of departures from classical statistical assumptions on linear regression estimates
Array. 3
Critically evaluate simple econometric analyses. 1D646D11-5694-0001-8A2D-1A6919901A2D
4. Design and implement an empirical investigation of an aspect of financial markets

Workload Full-time hours per semester
Type Hours Description
Lecture18Econometric methods: outline and explanation
Lecture12Exercises involving review of practical empirical work
Laboratory12Computer lab sessions
Online activity18Moodle: weekly exercises and quizzes
Assignment Completion3Project (week 5): desk work, lab work, and write-up
Assignment Completion12Empirical project: desk work, lab work, and write-up
Independent Study50Weekly 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:

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..

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 Assessment50% Examination Weight50%
Course Work Breakdown
TypeDescription% of totalAssessment Date
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
Indicative Reading List
  • Chris BROOKS: 2008, Introductory Econometrics for Finance, 2nd, Cambridge University Press,
  • 0: Jeffrey Wooldridge, Introductory Econometrics, A modern Approach, 5th edition, 87482
  • 0: Damodar N. Gujarati, Basic Econometrics, 4th edition,
Other Resources
15981, Moodle, 0, 15982, Econometric software: EViews, Stata, etc, 0,
Programme or List of Programmes
MITBMSc in Investment, Treasury & Banking
PBSSAPG Exchange(Business School)
PBSSAOPG Study Abroad(Business School)
Timetable this semester: Timetable for EF501
Date of Last Revision13-FEB-12