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Introduction to Econometrics

Module description

BEE1023 introduces students to the theory and application of econometrics.

This module requires students to have at least a Grade B in A Level Mathematics (or equivalent).

Students who wish to study this module must have taken one of BEE1022, BEE1025, BEA1012 or INT1003.

The module is not available to students who have studied MTH1004.

Additional Information:


The module introduces methods of empirical analysis which are frequently employed in academia and businesses across many disciplines and industries.

Research in Teaching

Students will study and replicate examples from various empirical research projects in economics

Full module specification

Module title:Introduction to Econometrics
Module code:BEE1023
Module level:1
Academic year:2023/4
Module lecturers:
  • Dr Eva Poen - Convenor
  • Dr Amy Binner - Lecturer
Module credit:15
ECTS value:



Grade B or higher in A-Level Mathematics (or equivalent)

BEE1022 or BEE1025 or BEA1012 or INT1003


Cannot be taken with MTH1004 (non-requisite)

Duration of module: Duration (weeks) - term 2:


Module aims

BEE1023 aims to familiarise students with bivariate and multivariate linear regression analysis. The contents of this module form the basis for all econometrics teaching in the undergraduate economics programmes.

ILO: Module-specific skills

  • 1. select, fit and interpret linear regression models in the context of economic theory;
  • 2. demonstrate understanding of estimators, their properties and sampling distributions under standard assumptions;
  • 3. perform and interpret hypothesis tests involving single coefficients, a linear combination of coefficients and multiple exclusion restrictions;
  • 4. demonstrate understanding of how software is used in econometric analysis.

ILO: Discipline-specific skills

  • 5. demonstrate awareness of the role and contribution that econometric methods make in the understanding of economic models;
  • 6. demonstrate awareness of the limitations of econometric methods;
  • 7. demonstrate ability to apply mathematical and statistical methods to economic problems;
  • 8. develop awareness of common difficulties arising in the analysis of economic data.

ILO: Personal and key skills

  • 9. develop data analysis skills and the ability to communicate about numerical evidence.

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity22Lectures
Scheduled Learning and Teaching Activity10Tutorials
Guided Independent Study118Reading and preparation for lectures, tutorials and assessments.

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Weekly tutorial exercises50 minutes1-9In class

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Five online homework quizzes 20approx. 30 minutes each1-9ELE
Mid-term test 201 hour1-2,4-9Final grade & feed back will be posted on ELE
Final examination 601.5 hours1-9Final grade & feed back will be posted on ELE

Details of re-assessment (where required by referral or deferral)

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Five online homework quizzes (20%)Single online homework quiz (20%)1-9August/September Reassessment Period
Mid-term test (20%)Examination (1 hour, 20%)1-2,4-9August/September Reassessment Period
Final examination (60%)Examination (1.5 hours, 60%)1-9August/September Reassessment Period

Re-assessment notes

If you pass the module overall, you will not be referred in any component – even if you have not passed one or more individual components.

Syllabus plan

  • The Nature of Econometrics
  • The Simple Regression Model
  • Multiple Regression Analysis: Estimation  
  • Multiple Regression Analysis: Hypothesis Testing and Confidence Intervals
  • Multiple Regression Analysis: Categorical variables and further issues 

Indicative learning resources - Basic reading

Mandatory reading: selected chapters of the textbook below

Wooldridge, J.M. (2020), Introductory Econometrics: A Modern Approach, 7th edition, CENGAGE Learning.

Previous editions of the same textbook may be used.

Module has an active ELE page?


Origin date


Last revision date