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2019/20 Undergraduate Module Catalogue

LUBS2570 Introduction to Econometrics

10 creditsClass Size: 270

Module manager: Dr Sandra Lancheros Torres

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2019/20

Pre-requisite qualifications

LUBS1280 - Mathematical Economics
LUBS1260 Mathematics for Economics and Business 1
OR both
MATH1710 - Probability and Statistics I and MATH1712 - Probability and Statistics II

This module is mutually exclusive with

LUBS2225Credit and Financial Analytics
LUBS2925Modelling Techniques for Business Analytics

This module is not approved as a discovery module

Module summary

This module provides you with an introductory knowledge of applied econometric techniques and relevant software. The module introduces the basic assumptions and interpretation of the linear regression with one regressor. It extends this model to incorporate additional regressors in the multivariate regression analysis. Additionally this module assesses the particular problems that may arise in regression analysis such as, multicollinearity, autocorrelation, heteroskedasticity and omitted variable bias.


The aim of this module is to introduce students to the basic tools of econometrics to enable them to use these techniques to test economic theory It also provides the basic explanation of the analysis of modern time series economic data.

Learning outcomes
Upon completion of this module students will be able to:
- Explain and identify basic applied econometric techniques, and econometric theories and methodologies
- Interpret the outcomes of econometric analysis
- Assess the reliability of the results from a regression analysis, namely to evaluate the external and internal validity of a regression analysis

Skills outcomes
Upon completion of this module students will be able to:
- Apply analytical and problem solving skills

Subject specific
- Apply econometric techniques and appropriate software to economic, accounting and financial analysis


Indicative content:
- The nature of econometrics
- The basic linear regression model
- Ordinary least squares (OLS)
- Interpretation & assumptions of basic models
- Multivariate regression analysis
- Problems in regression analysis
- Multicollinearity
- Autocorrelation
- Heteroscedasticity
- Omitted variables

Teaching methods

Delivery typeNumberLength hoursStudent hours
Computer Class51.005.00
Private study hours65.00
Total Contact hours35.00
Total hours (100hr per 10 credits)100.00

Opportunities for Formative Feedback

Progress monitoring will take place through the following routes:

- Individual feedback given in assignments for periodic seminars and computer sessions;

- Active communication using Announcements on the VLE;

- Practice exams (final exam) available via the VLE;

- Past exam papers with detailed solutions (final exam) available via the VLE;

- Access to teaching staff in weekly scheduled office hours;

Methods of assessment

Assessment typeNotes% of formal assessment
Computer ExerciseContinually assessed computer lab workshops and seminars30.00
Total percentage (Assessment Coursework)30.00

The resit for this module will be 100% by 1 hour examination.

Exam typeExam duration% of formal assessment
Standard exam (closed essays, MCQs etc)1 hr 00 mins70.00
Total percentage (Assessment Exams)70.00

The resit for this module will be 100% by 1 hour examination.

Reading list

The reading list is available from the Library website

Last updated: 30/04/2019


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