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.
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.
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.
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.
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.
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.
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”.
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.
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.