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: | 2021/2 |
| Module lecturers: |
|
| Module credit: | 15 |
| ECTS value: | 7.5 |
| Pre-requisites: | None |
| Co-requisites: | None |
| Duration of module: |
Duration (weeks) - term 1: 9 |
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 in R or STATA using different econometric software 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 Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching Activity | 18 | Lectures (9 x 2 hours) |
| Scheduled Learning and Teaching Activity | 18 | Tutorials (9 x 2 hours) |
| Guided Independent Study | 40 | Writing up reports from empirical analysis of real data |
| Guided Independent Study | 34 | Reading and research |
| Guided Independent Study | 40 | Learning and practicing the econometric software package. |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Weekly exercises | 3-5 questions | 1-6 | Verbal/Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Written Assignment 1 | 50 | 2,000 words | 1-6 | Written |
| Written Assignment 2 | 50 | 2,000 words | 1-6 | Written |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Written Assignment 1 (50%) | Written Assignment 1 (2,000 words, 50%) | 1-6 | August/September Reassessment Period |
| Written Assignment 2 (50%) | Written Assignment 2 (2,000 words 50%) | 1-6 | August/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
- Regression with panel data
- Nonlinear functions
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
Basic Reading:
There is no set text for this module, although I will base most of my lecture from Stock and Watson (2020). 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:
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),
Microeconometrics Using STATA, Revised Edition, 2010 (Stata Press)
Discovering statistics using R by Andy Field, Jeremy Miles and Zoe Field, 2012 (Sage)
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 or STATA
Origin date
29/09/2016
Last revision date
04/03/2021