ECONOMETRICS 2 - 2026/7
Module code: ECOM043
Module Overview
This module introduces students to microeconometric models for limited dependent variables, panel data methods, and causal inference techniques. A strong emphasis is placed on the empirical applications of these techniques and their intuition.
Module provider
Economics
Module Leader
SHIN Myungkou (Economics)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 66
Lecture Hours: 22
Laboratory Hours: 10
Guided Learning: 30
Captured Content: 22
Module Availability
Semester 2
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
- limited dependent variable models
- panel data models
- causal inference techniques
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| School-timetabled exam/test | CLASS TEST (60 MIN) | 40 |
| Coursework | INDIVIDUAL/GROUP PROJECT PAPER | 60 |
Alternative Assessment
None
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate the students' understanding of the statistical concepts associated with the methods studied, and ability to use STATA to solve simple problems can be assessed
Thus, the summative assessment for this module consists of:
- A class test.
- A individual/group project paper.
The class test allows students to demonstrate their understanding of statistical concepts and econometric models studied. The project paper allows students to demonstrate their ability to apply theoretical knowledge on empirical datasets, to select a suitable empirical strategy given economic questions, and to effectively communicate their findings through a written report, supported by tables and graphical illustrations where appropriate.
Formative assessment and feedback
Students receive verbal feedback on questions asked during the lectures and the computer-lab sessions. Datasets and code files for the computer-lab sessions are provided. In addition to this, they receive guideline solutions to practice questions, against which they can compare their own results.
Module aims
- introduce econometric theory on limited dependent variable, panel data models, and causal inference techniques
- develop competence in using statistical software to conduct empirical analysis.
Learning outcomes
| Attributes Developed | ||
| 003 | To be able to use statistical software in analysing empirical datasets and interpret the output | KP |
| 001 | To obtain knowledge on a range of microeconometric models | KC |
| 002 | To understand and critically evaluate applicability of the microeconometric models, given an economic question and an empirical context | KC |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- develop students' understanding of the intuition and ideas underlying the econometric methods studied.
- enable students to apply these methods to simple empirical problems.
- encourage active engagement through the preparation of exercises for discussion in lab sessions
- provide opportunities for students to interact during lectures and lab sessions.
The learning and teaching methods include:
- Lectures
- Computer-lab sessions
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ECOM043
Other information
In line with the University's curriculum framework, the School of Economics is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:
Resourcefulness and Resilience
The majority of the readings for the module are from text books and journal articles; addition, students have the opportunity to study and analyse data in their weekly computer lab sessions. Studying the former and completing the latter fosters resourcefulness. Resilience can be enhanced by reacting to feedback from the exercise sheets and through a degree of discussion during the lectures, tutorials and workshops.
Employability
The module promotes the use of econometric skills and interpretation of data. These are valuable in the workplace.
Digital Capabilities
By the end of the module, students are expected to have a high degree of proficiency in statistical softwares and to produce written solutions to a high presentational standard.
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Economics (Econometrics and Big Data) MSc | 2 | Compulsory | A weighted aggregate mark of 50% is required to pass the module |
| Economics MSc | 2 | Optional | A weighted aggregate mark of 50% is required to pass the module |
| Economics and Finance MSc | 2 | Optional | A weighted aggregate mark of 50% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.