EC9D7: Machine Learning and Big Data in Economics

Introduction

Analyses in all fields of Economics nowadays make frequent use of large and detailed datasets ("big data"). Their increased availability opens up 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, both in supervised and unsupervised methods, including machine-learning techniques Further discussion is provided on the applicability of those methods relative to other Econometric methods.

Principal Aims

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 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.

By the end of the module, students should feel comfortable with implementing both supervised

and unsupervised machine learning techniques for economic problems using state-of-the-art computational

tools. They should be aware of the interpretation of each technique, their statistical

properties and the assumptions required for the latter.

Principal Learning Outcomes

Subject Knowledge and Understanding: Be able to use a variety of modern data-science methods to solve economic questions. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Exam

Subject Knowledge and Understanding: Be able to use R to process data and apply data-science methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Exam

Subject Knowledge and Understanding: Understand under which conditions each method applies and be able to adapt their strategy to the problem studied. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Exam

Subject Knowledge and Understanding: Be able to use methods for both predictive and causal purposes. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Exam

Subject Knowledge and Understanding: Be able to understand, distinguish, and communicate the differences between correlational and causal analysis in the context of big data and machine learning methods. The teaching and learning methods that enable students to achieve this learning outcome are: Lectures, seminars, independent study. The summative assessment methods that measure the achievement of this learning outcome are: Exam

Syllabus

Context

Core Module

L1I1 - Year 1

Assessment

Assessment Method

Exam (100%)

Coursework Details

Exam (100%)

Exam Timing

May