The aims of this module is to build on the foundational knowledge from ECON212 and ECON213 by equipping students with a deeper understanding of big data analytics and advanced techniques used in data-driven projects and applied econometrics. Specifically, this module has two primary objectives:

Introduction to Advanced Topics
Introduce students to cutting-edge topics in big data analytics and advanced econometrics, drawing from textbooks as well as working or published papers. Students will gain exposure to novel techniques in big data analytics and contemporary econometric methods.

Critical Assessment and Replication
Challenge students to read, critically assess and potentially replicate a recent working paper or published paper in the field of data-driven research and modern econometrics using real data. Through this, students will develop the skills to effectively analyse and interpret academic research.

This module will equip students with the necessary knowledge and skills to undertake a comprehensive data project. They will learn to critically evaluate recent studies in big data analytics and modern econometric research and replicate these studies using real-world data.

By the end of the module, students will have developed a robust understanding of how to approach data-driven projects and conduct rigorous econometric research. Additionally, they will gain practical experience in applying advanced analytics and machine learning techniques to economic data, preparing them for research roles or data-intensive careers in academia, industry, or policymaking.

(LO1) Students will be able to demonstrate an understanding of and apply advanced econometric approaches, in rigorous analysis of economic and financial data.

(LO2) Students will demonstrate the ability to read and critically assess research papers (working and published papers), i.e. their ability to model data using an appropriate approach and analyse the results predicted by the econometric models.

(LO3) Students will demonstrate abilities in modelling data, using an appropriate approach and analysing the results predicted by their analysis.

(LO4) Students will demonstrate abilities in effective communication of econometric analysis, writing-up data projects and reports.

(LO5) Students will be able to demonstrate their awareness of the relationship between data-based analysis and published research; and construction of this type of knowledge (instilling confidence in students to continue into professional research or postgraduate study).

(S1) A Problem Solver

(S2) Numerate

(S3) An Excellent verbal and written communicator

(S4) A team player

(S5) IT Literate

(S6) Ethically Aware

Teaching Method 1: Lecture
Scheduled Directed Student Hours: 10
Description: Weeks 1-5 - 2hr lectures with learning directed to big data analytics and advanced econometric concepts which will be highlighted in the research papers.
Attendance Recorded: Yes

Teaching Method 2: Workshops
Scheduled Directed Student Hours: 5
Description: Weekly 1hr workshops to supporting the lectures, students will have an opportunity to discuss key themes around the big data analytics and advanced econometric covered and ask questions.
Attendance Recorded: Yes

Teaching Method 3: Seminar Presentations & Discussion
Scheduled Directed Student Hours: 6
Description: Weeks 6-8 - Students will read, present and critically assess the working paper/published paper suggested by the module leader. Each student will be asked to present a research paper. All students should attend the presentations and participate in the discussion. Overall participation is assessed .
Attendance Recorded: Yes

Teaching Method 4: Surgeries
Scheduled Directed Student Hours: 3
Description: Weeks 9-11 – During the group surgery, students will have the opportunity to ask questions and discuss the progress in their data project and gain formative feedback on the work discussed.
Attendance Recorded: Yes

Self-Directed Learning Hours: 128
Description: These independent learning hours are aimed at supporting the directed student learning. The module leader will provide guidance in the form of suggested readings (working or published papers). The student will have to read the suggested paper, critically assess it, and work in group to prepare a presentation on that paper. During their presentations students will receive comments that will help them prepare their final data project. Thus, Self-Directed Learning will include learning materials, develop presentation/communication skills, and develop academic writing skills.

Prerequis ites:
Students must have taken ECON212 and ECON213.

Skills Mapping:

Skill 1: A Problem solver
How is this developed: Students will understand how econometric analysis can be used to enlighten specific problems.
How is this assessed: Seminar Participation and Individual Data Project

Skill 2: Numerate
How is this developed: Students will use quantitative data to produce, analyse, evaluate and present econometric analysis.
How is this assessed: Seminar Participation and Individual Data Project

Skill 3: An Excellent verbal and written communicator
How is this developed: Students will read research papers (working and published) and critically assess them, which will help develop their own communication skills (then assessed in the data project report).
How is this assessed: Seminar Participation and Individual Data Project

Skill 4: A team player
How is this developed: Students will work in groups to present reviews of research papers.
How is this assessed: N/A

Skill 5: IT Literate
How is this developed: Students will develop further their skills in an econometrics computer package (EViews, Stata, R etc.)
How is this assessed: Seminar Participation and Individual Data Project

Skill 6: Ethically Aware
How is this developed: Students will be exposed to research papers using a range of economic data and how decisions regarding modelling assumptions can impact outcomes and the responsibilities of accurate reporting of these and findings (which has the potential for example to sway policy).
How is this assessed: Seminar Participation and Individual Data Project

The module takes a data project approach as, for the assessment, students will be asked to complete a data project. The syllabus will therefore contain three parts:

1. From week 1 to week 5 (5 weeks): Students will be introduced to new topics in big data analytics and advanced econometrics that can help them write a rigorous data project. Some of these topics are:
o Machine Learning Techniques: Lasso and Ridge regressions
o Modelling nonlinearity in economics and finance: Markov switching models
o Quantile regression analysis

2. From week 6 to week 8 (3 weeks): Students will be challenged to read, critically assess and discuss recent working or published papers on big data analysis and modern economic/econometric research.
o Weekly 2 hour seminars will provide students the opportunity to discuss working/published papers which might be related to the topics seen in the first part or other topics in applied econometrics which will help develop skills and abilities in the practice of big data and econometrics.
o Students will be required to contribute to the discussions. Specifically, each student will be asked to present a research paper, and to contribute to discussions around other students’ presentations. Participation in presentations and discussions constitutes 10% of student’s final mark.

3. From week 9 to week 11 (3 weeks): Students will have one hour surgery a week to ask their questions to the instructor.
o During each surgery, students will be allowed to discuss the progress in their individual data project (which constitutes 90% of student’s final mark).
o Discussions can be around anything regarding the data project for which they will be asked to replicate a paper or technique using real data.