Course Structure
The one-year program covers a wide range of core modules aimed at equipping students with state-of-the-art applied economics methods.
In their first semester, students will take modules in microeconomics, macroeconomics and econometrics. In their second semester, students will have the chance to choose various elective modules, drawn from different fields of theoretical and applied economics as well as modules in machine learning and quantitative text analysis.
All modules are taught around weekly lectures and tutorial sessions and assessed with applied continuous assessments (no exams or final dissertations are required). Successful applicants can expect coursework consisting of approximately 6 hours of lecture and 3 hours of tutorials per week. Students are expected to bring their own laptops (Mac/Windows/Linux) for use in lectures and tutorials throughout the course. Tablets are not suitable for this course.
Modules
- Introduction to Statistics and Regression Analysis (5 ECTS) – Core – This module introduces basic concepts of data analysis and statistics with practical applications to economics and policy. The emphasis is on the practical application of quantitative reasoning, visualization, and data analysis. The goal is to provide students with tools for conducting their basic statistical analyses. Topics covered include basic descriptive measures, probability, measures of association, sampling and sample size estimation, and confidence intervals. Assignments are based on real-world data and problems in a wide range of fields.
- Introduction to Big Data for Economics (5 ECTS) – Core – This module will introduce students to the main topics that have been arising around the concept of “Big Data”. With increasing computing and storage capacity, new opportunities and challenges arise for researchers. The goal of this module is to provide an overview of different papers, datasets and techniques, with an emphasis on practical implementation.
- Microeconometrics (10 ECTS) – Core – The aim of this module is to provide students with the skills required to undertake independent applied research using modern econometric methods. The module provides a balance between theoretical and applied econometrics and aims to extend students’ understanding of the subject to an advanced level as each part progresses. Students attending this module will deepen their theoretical knowledge of the list of topics above and will develop the necessary practical skills to use these methods in empirical research.
- Macroeconometrics (10 ECTS) – Core – The aim of this module is to provide students with the skills required to undertake independent applied research using modern econometric methods. The module builds on the fundamental concepts developed in Module I and aims to extend students’ understanding of the subject to a more advanced level. The module attempts to provide a balance between theory and applied research.
- Machine learning for economists (5 ECTS) – Core – The core objective of this module is to familiarise students with the application of machine learning techniques in economics, focusing on their integration with traditional econometric methods for effective policy analysis and causal inference. The module aims to promote an understanding of the strengths, limitations, and ethical considerations of these techniques, promoting the ability to use and critically evaluate machine learning tools in economic analysis and effectively communicate the results.
- Spatial Economics and Big Data (5 ECTS) – Elective – This module introduces students to key concepts when working with geo-spatial data and will show how they can be used to answer important economic questions across fields such as health, agriculture, urban and energy.
- Text analysis for macroeconomic policy (5 ECTS) – Elective – The learning aims for this module are to empower students with a deep understanding of the methods and techniques specifically tailored for macroeconomic analysis. Students will be trained on how to extract, process, and critically assess text data from statements by policymakers, central bank releases, speeches, and reports. This knowledge will provide unique insights into the relationship between central bank communication and financial markets, macroeconomic sentiment, and policy outcomes. The module seeks to equip students with the ability to integrate these methods into their economic research toolbox, enhancing their capacity to leverage unstructured data for robust economic analysis.
- Impact Evaluation and Big Data (5 ECTS) – Elective – What is impact evaluation? How can we neatly identify the causal impact of a certain program or policy or event on the outcomes of interest? Which tools do we have at our disposal? This module will address these questions, by focusing on the selection problem that typically arises in impact evaluation studies. We will discuss ways in which properly designed studies can address it. We will dedicate the first part of the module to discussing the design of sound experiments (randomized controlled trials). We will then discuss alternative solutions, for settings in which experiments are not feasible and/or desirable. Although the tools studied in this module have a broad application, examples and case studies will be taken mostly from the development economics literature.
- Labour markets and Big Data (5 ECTS) – Elective – The module will cover a range of topic in the field of Labour Economics. The central aim of the module is to understand how labour markets work, and how they are affected by institutions and labour market policies.
- Development Economics and Big Data (5 ECTS) – Elective – The module will cover recent contributions in the field of Development Economics. We will study how informal markets operate in developing countries and we will discuss the functioning of the credit sector, with a detailed analysis of microcredit and its development. Next, we will cover issues related to health, both in terms of demand for health and supply of health in a developing country context. We will then cover recent work conducted in the field of migration, with a focus on the role of information between migrants and their transnational networks. Finally, we will analyse recent contributions on the role of media in development. Throughout the module, there will be a strong emphasis on experimental settings. Active participation of students will be sought.
- Environmental Economics and Big Data (5 ECTS) – Elective – The aim of this module in Environmental Economics and Big Data is to familiarise students with the use of Big Data in addressing critical questions related to environmental economics. Students will explore how this extensive data can be effectively used for studying and solving complex issues such as climate change, the transition to a zero-carbon economy, and energy economics. The module aims to equip students with the skills and understanding necessary to harness big data's potential in environmental economic research and policy-making, promoting a data-driven approach to sustainable economic development.
- Financial Markets and Big Data (5 ECTS) – Elective – The learning aims of this module in Financial Markets and Big Data are to enable students to understand the relationship between pricing and risk in traditional assets as well as in emerging financial entities like cryptocurrencies, NFTs, and digital currencies. The module intends to equip students with the ability to leverage Big Data for comprehensive analysis of these markets, enhancing their understanding of pricing mechanisms and risk assessment. Through this module, students will gain insights into the integration of Big Data in financial markets, fostering a data-driven approach in their financial research and market strategy planning.
- Quantitative Text Analysis for Social Scientists (5 ECTS) – Elective – This module focuses on a range of computational tools - stemming from the fields of machine learning and natural language processing (NLP) - that are essential for large-scale analyses of text information. The aim is to provide students with a hands-on introduction to collecting, processing, and analysing “text-as-data” for the purpose of answering important social science research questions. The module will also cover corpus acquisition methods as well as social media research applications. Students will apply these skills to produce state-of-the-art analyses based on a novel collection of text documents and meta-data.