Course Overview
Timberlake's Applied Econometrics MSc programme is built to be flexible. It allows you to tailor your learning to your specific interests, learning online around your career. Best-in-field experts lead all specialist courses for unrivalled quality.
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There are three core modules and 14 electives to choose from. You can join us for as little as one module, or build up to a full Masters of Science (MSc). In addition, we offer optional specialisms if you would like to focus on Machine Learning, Time Series and Forecasting, or Microeconometrics and Empirical Economics.
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Core Modules
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Understand the fundamental concepts of time series econometrics and forecasting. Gain the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities.
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20 Credits
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Gain an in-depth understanding of panel data econometrics, presented from a microeconometrics perspective. The course will cover linear panel data models with unobserved heterogeneity, including discussions of the strengths and weakness of the various estimation methods.
20 Credits
Understand the fundamental concepts of time series econometrics and forecasting. Gain the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities.
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20 Credits
Electives
The course aims to develop students' econometric skills and provide practical guidance on how to forecast.
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10 Credits
This course will show how time series can be modelled and analysed. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details.
10 Credits
The purpose of this course is to provide students with an introduction to automatic model selection and its limitations and uses in practical econometric modelling.
10 Credits
This course will provide an introduction to simulation-based methods that are commonly used in microeconometrics. The emphasis will be Bayesian, although we will also contrast posterior analysis with maximum likelihood estimation.
10 Credits
The purpose of this course is to provide students with an introduction to data mining and how to best handle big data. Most applied research rely on data that is only getting larger and more complicated.
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10 Credits
The course covers the estimation and usage of discrete choice models that are increasingly estimated using simulation methods. Discrete choice models are used to examine the choices of individual consumers, households, firms and other agents.
10 Credits
This course will provide the students with an in-depth understanding of the fundamental concepts of econometric production analysis and with the practical skills to use econometric software to empirically analyse production technologies and producer behaviour.
10 Credits
This course will show how energy returns can be modelled analysed and forecasted. The aim is to provide understanding and insight into the methods used, as well as explaining the technical details.
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10 Credits
This course provides students with an introduction to various methods applicable to high-frequency financial data. This includes the study of the statistical properties of these series such as heteroskedasticity, periodicity, the presence of jumps and microstructure noise.
10 Credits
This course will provide participants with the essential tools, both theoretical and applied, for a proper use of modern microeconometric methods for policy evaluation and causal counterfactual modelling.
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10 Credits
The aim of this course is to introduce students to machine learning, which is a relatively new approach to data analytics at the intersection between statistics, computer science, and artificial intelligence. Students will be taught how to master the theory and the techniques that allow turning information into knowledge and value by “letting the data speak”.
10 Credits
Get a comprehensive introduction to Monte Carlo methods, as used in econometrics. Most research papers use Monte Carlo, and we aim to understand its use and limitations.
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10 Credits
The purpose of this course is to introduce students to the theory and practice of applying natural language processing (NLP) in economics and business. We cover all steps in the data science pipeline of transforming textual data into numbers that are relevant for decision making.
10 Credits
The aim of this course is to provide students with programming skills that can be used to perform data management operations and several types of statistical and econometric analysis in an efficient and reproducible way.
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10 Credits
The aim of this course is to provide students with programming skills that can be used to perform data management operations and several types of statistical and econometric analysis in an efficient and reproducible way.
10 Credits
This course is an introduction to the principles and practices of causal machine learning. It covers the basics of causal inference, including the identification and estimation of causal effects, as well as the applications of causal machine learning in areas such as health, finance, and social sciences. The course also includes an introduction to common methods and algorithms for causal machine learning, such as instrumental variables, synthetic controls, AB-testing and propensity scores.
10 Credits