Module code: ECOM042

Module provider


Module Leader

GABRIEL V Dr (Economics)

Number of Credits


ECT Credits



FHEQ Level 7

JACs code


Module cap (Maximum number of students)


Module Availability

Semester 1

Overall student workload

Independent Study Hours: 113

Lecture Hours: 20

Laboratory Hours: 9

Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test IN-SEMESTER TEST - 50 MINUTE LAB TEST 50%
Examination EXAMINATION - 1 HOUR 30 MIN 50%

Alternative Assessment

Coursework exercise which can be completed off-campus.

Prerequisites / Co-requisites


Module overview

This module is an introduction to the methods of specification, estimation and testing of econometric models in a general multivariate setting. The techniques are applied to real data making use of the econometric packages EViews and Stata.

Module aims

Provide the student with the theoretical and practical skills necessary to construct state of the art, single and multi-equation econometric models. The module will equip the student with the ability to undertake, understand, and critically assess empirical work in economics, with a view to enabling the student to use econometrics to catalogue and describe empirical regularities and test various propositions.

Learning outcomes

Attributes Developed
Understand the principles of estimation and hypothesis testing in a multivariate setting KCT
Know the properties of different estimators and tests KCT
Be able to apply econometric techniques to actual data using computer packages KCPT
Be critically aware of the assumptions made in building econometric models KCT
Write up the results of a study of an economic problem that includes econometric analysis KCPT
Proficiently use the time series testing and estimation capabilities of a range of packages KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Module content

Indicative content includes:

Multiple Regression analysis using cross sectional data. 
Asymptotic properties of OLS
Regression analysis using qualitative information. 
Functional form.
Instrumental variables estimation
Econometric models with time series

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

prepare the students for the study of economics and econometrics at FHEQ Level 7 (first week)
give students the theoretical tools they need to go out and analyse real world situations;
encourage rigour in their approach to problems;
encourage hands-on study of empirical problems;

The learning and teaching methods include:

readings using lecturers guidance
solving exercises
responding to questions in class
preparing and taking part in the test
4.5 hours of lectures per week x 1 week
2 hour lecture per week x 10 weeks
1 hour lab classes x 9 weeks

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate their ability to understand and carry out econometric techniques.

This module has a technical and a practical component. The latter is, at this level, more important. As such, assessment emphasises work based on econometric and statistical packages (mainly EViews) in the form of an lab test (delivered via SurreyLearn), in which students are asked to analyse real economic and financial data. The technical component is assessed via a final examination.

Thus, the summative assessment for this module consists of:

50% In-semester test: 50 minute lab test, typically in Week 7, which includes the use of an econometric package

50% Examination in Week 13-14: 1 hour and 30 minutes

Formative assessment and feedback

This is done by specific, individualised written comments, feedback meetings with students and general feedback in classes.

Reading list

Reading list for ECONOMETRICS 1 :

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.