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Module

Applied Econometrics 1

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.  

Full module specification

Module title:Applied Econometrics 1
Module code:BEEM011
Module level:M
Academic year:2023/4
Module lecturers:
  • Dr Amy Binner -
Module credit:15
ECTS value:

7.5

Pre-requisites:

None

Co-requisites:

None

Duration of module: Duration (weeks) - term 1:

9

Duration (weeks) - term 2:

0

Duration (weeks) - term 3:

0

Module aims

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

ILO: Module-specific skills

  • 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

  • 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

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

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad
361140

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
10000

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

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.

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 https://www.econometrics-with-r.org/index.html. 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)

Module has an active ELE page?

Yes

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

Origin date

29/09/2016

Last revision date

09/05/2023