BSc Economics

Year of entry: 2022

Course unit details:
Econometrics and Data Science

Course unit fact file
Unit code ECON32252
Credit rating 20
Unit level Level 3
Teaching period(s) Semester 2
Offered by
Available as a free choice unit? Yes

Overview

This course will provide a solid grounding in recent developments in applied micro-econometrics, including state-of-the art methods of applied econometric analysis and elements of machine learning. Some of these methods are related to work by recent Nobel Prize in Economics winners J. Angrist, D. Card and G. Imbens.

Pre/co-requisites

Unit title Unit code Requirement type Description
Advanced Statistics ECON10072 Pre-Requisite Compulsory
Econometrics ECON20110 Pre-Requisite Compulsory
Quantitative Methods ECON20222 Pre-Requisite Compulsory
Advanced Statistics ECON20072 Pre-Requisite Compulsory
Advanced Statistics ECON10072B Pre-Requisite Compulsory

(ECON20110 Econometrics or ECON20222 Quantitative Economics) and (ECON10072A or B Advanced Statistics or ECON20072 Advanced Statistics)

Only open to students on BSc Economics, BA Econ, PPE, IBFE and Modern History with Economics who meet the above pre-requisites.

 

Aims

This course will provide a solid grounding in recent developments in applied micro-econometrics, including state-of-the art methods of applied econometric analysis and elements of machine learning. Some of these methods are related to work by recent Nobel Prize in Economics winners J. Angrist, D. Card and G. Imbens.

The course will combine both analytical and computer-based (data) material to enable students to gain practical experience in analysing a wide variety of econometric problems. It will also discuss how modern data science approaches can be used to answer important economic questions. Students will be reading various applied economic papers which apply the techniques being taught. Applications that will be considered include labour, development, industrial organisation and finance.

Syllabus

An indicative list of topics is 
•    matching methods, 
•    identification of average, local average and marginal treatment effects using instrumental variables, regression discontinuity, 
•    maximum likelihood estimation, 
•    machine learning methods for causal inference and prediction (ridge regression, lasso regression, support vector machines, random forests)
•    bootstrap

If time permits, the following topics may also be covered:  
•    randomised control experiments, 
•    post-estimation diagnostics

Teaching and learning methods

20 (200h student work)
Lectures: 20h (10 weeks @ 2h)
Exercise Classes: 6 classes

New content will be delivered to students asynchronously (reading) as well as synchronously through lectures. The virtual learning environment (VLE) will clearly guide students through the different sources of content. 

Students’ learning will be supported by offering regular computing exercise classes using the software R. These exercises will not only help to develop the students’ programming skills but also help students gain a deeper understanding of material taught.

Exercise classes are an important source of formative feedback for students as they will be able to assess their state of understanding of the material through engagement in tutorials. In addition, students will be able to use a discussion board in which they can test their understanding as well as ask questions to their peers and the teaching staff.

In addition to these formative feedback opportunities, the in-term assessments (see below) will provide summative and formative feedback to students.
 

Knowledge and understanding

  • Understand how empirical data can be used to analyse economic problems and questions
  • Understand advantages and limitations of the various statistical methods covered in the course unit.
  • Implement empirical techniques using the statistical software R
  • Be able to independently read current empirical research in Applied Economics as well as communicating it to a non-economist audience

Intellectual skills

  • Critically evaluate applied work in the fields of Econometrics and Data Science

Practical skills

  • Independently identify and assess relevant literature
  • Identify suitable technique(s) relevant to research questions in the general fields of economics and business.
  • Develop advanced programming skills
  • Be able to develop and implement an empirical project to answer a research question of interest

Transferable skills and personal qualities

  • Concisely summarise empirical results and compile research reports.
  • Be able to evaluate the design of policies by drawing on the discussions and methods introduced in this module
  • Present research, empirical results, and policy evaluations

Assessment methods

10% Homework (computer exercise and theoretical exercises)

40% Empirical Project, 3000words

50% Exam

Recommended reading

Primary readings for the course are the following: 

Scott Cunningham (2021). Causal Inference: The mixtape. (Yale University Press) -- a free online HTML version available at https://mixtape.scunning.com/

Gareth James, Daniela Witte, Trevor Hastie and Robert Tibshirani, (2017). An Introduction to Statistical Learning: With Application in R, 2nd edition (Springer) – a free copy available at https://www.statlearning.com/

Angrist, J.D. and J.-S.Pischke (2009), Mostly Harmless Econometrics, Princeton.

Wooldridge, J. (2019) Introductory Econometrics, A Modern Approach, Cengage.

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