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EC340: Topics in Applied Economics (3a)

  • Pedro Souza

    Module Leader
  • Roland Rathelot

    Module Lecturer
15 CATS - Department of Economics
Spring Module
Autumn Module

Principal Aims

Analyses in all fields of Economics nowadays make frequent use of large and detailed datasets ("big data"). The explosion in data access and availability opens many opportunities for applied research, as well as new challenges on how to handle, process, and extract meaningful conclusions from the data. This module provides an overview of recent developments in econometric methods tailored to handle such large datasets, such as machine learning techniques, and articulates the use of those methods to the problem of causal identification of treatment effects.

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

Syllabus

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

- Recommendation systems

Context

Optional Module
L100 - Year 3, L103 - Year 4, L116 - Year 3, L117 - Year 4, LM1D (LLD2) - Year 3, LM1H - Year 4, V7ML - Year 3, V7MM - Year 4, V7MP - Year 3, V7MR - Year 3, GL11 - Year 3, GL12 - Year 4, L1P5 - Year 1, L1PA - Year 1, LA99 - Year 3, R9L1 - Year 4, R3L4 - Year 4, R4L1 - Year 4, R2L4 - Year 4, R1L4 - Year 4, L1L8 - Year 3
Part-year Availability for Visiting Students
Not available on a part-year basis

Assessment

Assessment Method
Coursework (100%)
Coursework Details
Essay (100%)
Exam Timing
N/A

Exam Rubric

N/A

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.

Reading Lists