果冻传媒

Introduction

Economists and econometricians have developed further tools that are particularly well-suited for the use of datasets in economics and to answer questions that are interesting for economists. This is because many such questions require tools for both prediction and causality, and try to develop ways to conduct hypothesis testing. This module introduces students to advanced topics in this intersection of data science with economics. Such topics may include (i) entropy measurement, (ii) applications of machine learning in text analysis in economics, (iii) causal Machine Learning, (iv) resampling methods, and (v) estimation of network formation and other non-linear panel data models with large datasets.

Principal Aims

The module's main aims are to help students:

- gain an understanding of recent develops of methods, such as those for entropy measurement, network formation, resampling methods and causal machine learning.

- develop critical thinking about the types of questions requiring each method

- develop an ability to apply those tools for relevant applied problems.

Principal Learning Outcomes

Subject Knowledge and Understanding: demonstrate awareness and understanding of recent developments in methods in the intersection of data science and economics, particularly for transforming data and causal inference using large datasets.

Subject-specific skills/Professional Skills:...gain an understanding for and an ability to differentiate the appropriateness of different statistical methods. T

Syllabus

Context

Optional Module

L1I1 - Year 1

Pre or Co-requisites

Foundations of Data Science; Machine Learning and Big Data in Economics (concurrent) Students should be taking EC910 Econometrics B to enrol on this module.

Assessment

Assessment Method

Coursework (100%)

Coursework Details

Presentation (20%) , Project (80%)

Exam Timing

N/A