The aim of the module is to introduce students to the analytical tools and the knowledge to study economic problems using modern data science methods. The module covers up-to-date econometric techniques in big data and machine learning, as well as the challenge posed by identification of causal parameters of interest. The aim is to present the econometric techniques along with the hands-on implementation in the computer language R. The module suggests a
number of interesting applications in Economics.
Principal Learning Outcomes
By the end of the module students should: - Be able to use a variety of modern data-science methods to solve economic questions. - Be able to use R to process data and apply data-science methods. - Understand under which conditions each method applies and be able to adapt their strategy to the problem studied. - Be able to use methods for both predictive and causal purposes. - Develop and enhance computer skills in the R language, including the writing of clear and reproducible R codes - Be able to understand, distinguish, and communicate the differences between correlational and causal analysis in the context of big data and machine learning methods - Be able to process and work efficiently with large datasets
The module will typically cover some of the following topics: Methods:
Principal Components and Neural Networks
Lasso, Adaptive Lasso, Elastic Net, Penalized Logistic Regression
Random Forest, Regression trees Economic applications:
Policy evaluation and heterogenous treatment effects
Time series, forecasting, VAR
Topic modelling, text analysis
- Optional Module
- LM1D (LLD2) - Year 3, L116 - Year 3, V7ML - Year 3, LA99 - Year 3, L100 - Year 3, L1L8 - Year 3, R9L1 - Year 4, R3L4 - Year 4, R4L1 - Year 4, R2L4 - Year 4, R1L4 - Year 4, V7MM - Year 4, V7MP - Year 3, L1P5 - Year 1, L1PA - Year 1, V7MR - Year 3, LM1H - Year 4, GL12 - Year 4, GL11 - Year 3, L103 - Year 4
- Pre or Co-requisites
- EC203 or EC226 and either EC108 + EC109 or EC107
- Not available to non-final year students on Economics-based degrees.
- Part-year Availability for Visiting Students
- Not available on a part-year basis
- Assessment Method
- 2 hour computer based in-class test (100%)
- Exam Timing
Previous exam papers can be found in the University’s past papers archive. Please note that previous exam papers may not have operated under the same exam rubric or assessment weightings as those for the current academic year. The content of past papers may also be different.