Introductory description

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.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

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

Learning outcomes

By the end of the module, students should be able to:

• 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

Research element

Module suggests avenues for future research in Economics, as well as recent applications and development of econometric methods in the area of study

Interdisciplinary

Module covers contents that are relevant to also disciplines including Computer Sciences and Statistics. Applications of the method might involve other disciplines.

International

Skills learned in the module may facilitate employment opportunities worldwide. Applications of the methods presented in the module may be drawn from international experiences.

Subject specific skills

• Develop and enhance computer and coding skills in the open-source language R
• Enhance the capacity to conduct economic analyses autonomously
• Develop skills in processing and handling large datasets
• Be able to understand, distinguish, and communicate the differences between correlational and causal analysis in the context of big data and machine learning methods

Transferable skills

• Develop and enhance computer and coding skills in the open-source language R
• Enhance the capacity to conduct economic analyses autonomously
• Develop skills in processing and handling large datasets
• Be able to understand, distinguish, and communicate the differences between correlational and causal analysis in the context of big data and machine learning methods