Module Timetable
Take a look at the modules currently scheduled throughout this year on our Applied Econometrics Postgraduate Programme.
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Join industry experts for as little as one module, or build up to our full online MSc program. We operate a pay-as-you-go model to make your learning as flexible as possible around you.
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Learn how to make your data shine, accelerate your career in Applied Econometrics and beyond.
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*Please note the below timetable may be subject to minor changes
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Term 1: 6 October 2023 – 15 December 2023
Apply now to study these modules
The purpose of this core course is to provide students with an introduction to statistics and econometrics, to successfully study a complete course in econometrics. In addition, the course is designed to enable students to analyse the classical linear regression model, its statistical foundations, and its various estimation techniques.
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20 Credits
Time Series Econometrics and Forecasting
Prof Alain Hecq, Prof Christian Francq & Prof Jean-Michel Zakoïan
The purpose of this core course is to provide students with an in-depth understanding of the fundamental concepts of time series econometrics and forecasting and with the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities. The module would build on the foundation of econometrics core course and prepare students for MSc and PhD research.
20 Credits
The purpose of this course is to provide students with an introduction to Monte Carlo methods as used in econometrics. Most research papers use Monte Carlo, and we aim to understand its use and limitations. We use the Oxlanguage to understand and create Monte Carlo experiments. Ox is an object-oriented matrix programming language with a mathematical and statistical function library, developed by Dr Jurgen Doornik and widely used in different econometrics specialisations.
10 Credits
Term 2: 12 January 2024 – 22 March 2024
The purpose of this core course is to provide students with an introduction to statistics and econometrics, to successfully study a complete course in econometrics. In addition, the course is designed to enable students to analyse the classical linear regression model, its statistical foundations, and its various estimation techniques.
20 Credits
Time Series Econometrics and Forecasting
Prof Alain Hecq, Prof Christian Francq & Prof Jean-Michel Zakoïan
The purpose of this core course is to provide students with an in-depth understanding of the fundamental concepts of time series econometrics and forecasting and with the practical skills to use econometric software to model and forecast economic time series and identify models with the best forecasting abilities. The module would build on the foundation of econometrics core course and prepare students for MSc and PhD research.
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.
10 Credits
The purpose of this course is to provide students with 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
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. In addition, the course introduces saturation estimation techniques and considers other approaches.
10 Credits
Term 3: 19 April 2024 – 28 June 2024
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. Statistical time series modelling will be demonstrated using the STAMP computer package and participants will be given the opportunity to use STAMP in class.
The Time Series Lab (TSL) program enables the score-driven approach to nonlinear time series to be implemented. There will be a wide range of applications, ranging from assessing the impact of the UK seat belt law, modelling volatility in financial time series and predicting the spread of coronavirus.
10 Credits
This course will provide participants with the essential tools, both theoretical and applied for proper use of modern microeconometric methods for policy evaluation and causal
counterfactual modelling.
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Each student should be able to read and understand applications from different policy sub-fields, such as finance and banking, the labour market, the investment activities of enterprises, education policy and regional cooperation, incentives for business research and development, etc.
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. Students will be taught the fundamentals of programming in Stata. The module will prepare students for PhD research or work as an econometrician consultant in public and private organisations.
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10 Credits
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 relies on data that is only getting larger and more complicated. Researchers have to first clean, manage and mine the data to better understand any existing relationships and guide analysis. This course is aimed towards research and public policy applications.
10 Credits
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”.
The teaching approach will be based on graphical language and intuition more than on algebra. The course will make use of instructional as well as real-world examples and will balance theory and practical sessions with the software Stata.
10 Credits
The course aims to develop students' econometric skills and provide practical guidance on how to forecast. Economics needs to forecast a non-stationary and evolving world, using a forecasting model that differs from the economic mechanism. The resulting framework, its basic concepts and main implications are sketched. Many famous theorems of economic forecasting no longer hold—rather, their converses often do.
10 Credits
This course introduces programming in Python. It covers the basics of programming skills, including data types, variables, loops and functions. Further, the course covers common Python libraries and frameworks for data manipulation, visualisation, and machine learning.
10 Credits
Term 4: 1 July 2024 – 23 August 2024
The purpose of this course is to provide 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.
Different methods will be discussed such as GARCH models, the estimation of the intraday periodicity in volatility, jumps tests but also various non-parametric estimators of the class of the realized volatility
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. The course will cover the main discrete choice models and a variety of specifications that build on these models, as well as standard maximum likelihood and simulation-based estimation techniques. Discrete choice models are applicable in many fields, including energy, environmental studies, health, labour, marketing, urban economics and transportation.
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.Despite the technical content, the course begins with an introduction to the Bayesian paradigm and introduces key concepts and vernacular
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. The module will build on the foundation of core econometrics courses and prepare students for MSc and PhD research or work as an econometrician, consultant, or quantitative analyst for public and private institutions within the broad area of econometric production analysis.
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. Econometric modelling and forecasting will be demonstrated using the EViews and Matlab and participants will be given the opportunity to learn these software's in class. The course will discuss univariate and multivariate GARCH models for energy returns and the Value at Risk and Global Minimum Variance portfolio used by policy markets, traders and academics to identify the best econometric model of energy returns
10 Credits
10 Credits
10 Credits