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EC9D7: Machine Learning and Big Data in Economics

  • Pedro Souza

    Module Leader
18 CATS - Department of Economics

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

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: Project and test.

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: Project and test.

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: Project and test.

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: Project and test.

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: Project and test.

Syllabus

• Principal Components and Neural Networks

• Lasso, Adaptive Lasso, Elastic Net, Penalized Logistic Regression

• Random Forest, Regression trees

• Policy evaluation and heterogenous treatment effects

• Time series, forecasting, VAR

• Topic modelling, text analysis

• Recommendation systems

Context

Assessment

Assessment Method
Coursework (100%)
Coursework Details
Project , Computer-based
Exam Timing
N/A