The Applied Econometrics module is a practical course that aims to enhance students' empirical economic analysis skills. With the ever-growing volume of economic and financial data and its increasing use to inform decision-making, there is a pressing need for economists in the private and public sectors, as well as in academia, to perform empirical analysis. This course provides students with hands-on training in applying the most widely used econometric analysis techniques using a statistical analysis software and associated coding language. By doing so, students will be well-equipped to conduct empirical economic analysis in preparation for becoming full-fledged economists.
This module will equip students with practical and critical thinking empirical skills which are highly sought after in the labour market. They are especially important for students who want to work as economists in the private or public sector, or in any profession where data reports are used, and they are essential for students aspiring to pursue academic careers. The coding component of the course will also serve as a first or additional programming training for students interested in data science-related careers.
The applications discussed in the module are based on examples and datasets from around the world.
Full module specification
|Module title:||Applied Econometrics|
BEE2031 or BEE2006
|Duration of module:||
Duration (weeks) - term 1: |
0Duration (weeks) - term 2:
11Duration (weeks) - term 3:
This module aims to provide students with a high degree of understanding as well as extensive hands-on experience in implementing and analysing the most often used empirical analysis techniques in economics. The module will focus on data skills (organisation and manipulation of data, descriptive and explorative analysis), and applied econometrics (regression models for cross-sectional, panel and time series data). You will learn to apply those techniques using one of the main statistics software.
ILO: Module-specific skills
- 1. Demonstrate ability to understand the important features and properties of economic data, and perform the necessary operations to organise and manipulate the data.
- 2. Apply appropriate quantitative techniques based on the research question and features of the data.
- 3. Perform independent empirical analysis using a statistical software.
ILO: Discipline-specific skills
- 4. Show proficiency in using quantitative and data skills to apply knowledge from economics and econometrics theories to complete an empirical research work.
ILO: Personal and key skills
- 5. Demonstrate analytical and critical thinking.
- 6. Demonstrate the ability to program within a statistical software.
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Schedule learning and teaching||22 hours||Lectures|
|Scheduled leaning and teaching||10 hours||Tutorials|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|In class discussion/exercises||Weeks 1 to 11||1-6||Verbal/ELE|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Practical Exam||80||3 hours||1-6||Discussion|
|Homework tasks||20||Approx. 6 pages A4||1-6||ELE/Turnitin|
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|
|Practical Exam||Practical Exam (80%)||1-6||August Examination Period|
|Homework tasks||Homework tasks (20%)||1-6||August Examination Period|
• Introduction to Stata
• Exploratory Data Analysis
• Linear Regression Analysis
• Functional Form / Interaction Effects
• Missing Data
• Binary Dependent Variables
• Marginal Effects
• Panel Data
• Time Series
Indicative learning resources - Basic reading
The module does not rely on a textbook. All relevant material will be presented in class and in the lecture notes. Additional reading may be given for certain topics.
- The theoretical content is covered in Jeffrey M. Wooldridge, Introductory Econometrics, 7th Edition (2020), Cengage.
- More practical content with examples and applications in Stata can be found here:
- Cameron, A.C and Trivedi, P.K. (2022) Microeconometrics using Stata, 2nd revised edition, Stata Press.
- Hill, R.C, Griffiths, W.E and Lim, G.C (2018) Principles of Econometrics, 5th edition, Wiley.
- Adkins, L.C. and Hill, R.C. (2018) Using Stata for Principles of Econometrics, 5th edition, Wiley.
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
UK Data Service (http://ukdataservice.ac.uk/ )
Indicative learning resources - Other resources
A Gentle Introduction to Stata by Alan C. Acock
Data Analysis Using Stata by Ulrich Kohler and Frauke Kreuter
Interpreting and Visualizing Regression Models Using Stata by Michael N. Mitchell
An Introduction to Stata Programming by Christopher F. Baum
Last revision date