Applied Econometrics 1
| Module title | Applied Econometrics 1 |
|---|---|
| Module code | BEEM011 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Amy Binner (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 | 0 | 0 |
| Number students taking module (anticipated) | 180 |
|---|
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 Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 40 | 110 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 22 | Lectures (9 x 2 hours) |
| Scheduled learning and teaching activities | 9 | Tutorials (9 x 2 hours) |
| Scheduled learning and teaching activities | 9 | Online Code and Coffee (9 x 1 hour) |
| Guided independent study | 54 | Reading and research |
| Guided independent study | 60 | Learning and practicing the econometric software package |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Tutorial 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 |
|---|---|---|---|---|
| Three homework submissions | 10 | 3 x approx. 45 mins | 1-6 | ELE |
| Midterm test | 20 | 1 hour | 1-6 | Final grade and feedback will be posted on ELE |
| Written assignment | 70 | 2,000 words | 1-6 | Final grade and feedback via ELE |
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 |
|---|---|---|---|
| three online submissions | Single online quiz (10%) | 1-6 | August/September reassessment period |
| Midetrm test | Midterm test (20%) | 1-6 | August/September reassessment period |
| Written assignment 1 (70%) | Written assignment 2 (2,000 words 70%) | 1-6 | August/September reassessment period |
Indicative learning resources - Basic reading
-
Introduction to Econometrics by James Stock and Mark Watson, 4th Edition (Global Edition), 2020 (Pearson International)
-
Applied Econometrics with R by Christian Kleiber and Achim Zeileis, 2008 (Springer Science & Business Media)
-
Mostly Harmless Econometrics by Joshua Angrist and Jorn-Steffen Pischke, 2009 (Princeton University)
Other resources that are useful references 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)
-
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)
For more advanced econometrics readings:
-
Econometric Analysis by William H. Greene, 8thEdition 2017 (Pearson)
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, VS Code (for macOS users)
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | Yes |
| Origin date | 29/09/2016 |
| Last revision date | 11/04/2024 |
