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University of Exeter Business School

Applied Econometrics 1

Module titleApplied Econometrics 1
Module codeBEEM011
Academic year2023/4
Module staff

Dr Amy Binner ()

Duration: Term123
Duration: Weeks




Number students taking module (anticipated)


Module description

Applied econometrics employs statistical methods to real-world data to give a quantitative description of the relationship amongst variables around us and a measure of how precise this description is. In this module, we will briefly review probability theory and fundamental statistics, which will cover topics like hypothesis testing and confidence intervals. We will then proceed to regression analysis, which is the workhorse of applied econometrics. We will also attempt to cover more advanced topics in regression analysis such as, but not limited to, panel data methods and nonlinear functions.  

Module aims - intentions of the module

The module aims to provide students with an applied econometric foundation necessary in order to conduct a high-standard empirical analysis of economic data.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

  • 1. Demonstrate aptitude in the econometric techniques to analyse economic data
  • 2. Exhibit technical expertise to analyse the data using Jupyter Notebook with R econometric packages

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

  • 3. Formulate hypotheses of interest, derive the necessary tools to test these hypotheses and interpret the results
  • 4. Demonstrate a specialised knowledge of linking the theory and empirical questions

ILO: Personal and key skills

On successfully completing the module you will be able to...

  • 5. Solve analytical problems and provide appropriate interpretation of the outcomes for decision making
  • 6. Demonstrate data analysis skills

Syllabus plan

The syllabus plan is as follows:

  • Review of probability and statistics
  • Fundamentals of regression analysis
  • Further topics in regression analysis
    • Nonlinear functions
    • Panel data methods


The convenor and the university reserve the right to modify elements of the course during the term. It is the responsibility of the student to check his/her email and course websites weekly during the term to note any changes.

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 activities18Lectures (9 x 2 hours)
Scheduled learning and teaching activities18Tutorials (9 x 2 hours)
Guided independent study40Writing up reports from empirical analysis of real data
Guided independent study34Reading and research
Guided independent study40Learning and practicing the econometric software package

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Tutorial exercises3-5 questions1-6Verbal/Written

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
Six homework submissions306 x approx. 45 mins1-6ELE
Written assignment 702,000 words1-6Final grade and feedback via ELE

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Six online submissions (30%)Single online quiz (30%)1-6August/September reassessment period
Written assignment 1 (70%)Written assignment 2 (2,000 words 70%)1-6August/September reassessment period

Indicative learning resources - Basic reading

The lecture notes and slides will be available and uploaded on ELE: 

  • Introduction to Econometrics by James Stock and Mark Watson, 4th Edition (Global Edition), 2020 (Pearson International)

Other resources that are useful reference to study methods in this course include the following:

  • Applied Econometrics with R by Christian Kleiber and Achim Zeileis, 2008 (Springer Science & Business Media)
  • Introduction to Econometrics with R by Christoph Hanck, Martin Arnold, Alexander Gerber and Martin Schmelzer (2019) – accessible at This book is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
  • Basic Econometrics by Damodar N. Gujarati, 2009 (McGram Hill)
  • Introduction to Econometrics by Christopher Dougherty, 2016 (Oxford)
  • Discovering statistics using R by Andy Field, Jeremy Miles and Zoe Field, 2012 (Sage)
  • Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge, 2018 (South Western College)

Indicative learning resources - Web based and electronic resources

  • ELE – College to provide hyperlink to appropriate pages

Indicative learning resources - Other resources

R, R-studio, Jupyter Notebook

Key words search

Econometrics, Data Analysis, Linear Regression, R, STATA

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date