Forecasting: Methods, Evaluation, and Applications

This event is part of the Specialist Training Series on Machine Learning and Data Science delivered by the South & East Network for Social Sciences Doctoral Training Partnership (SENSS).

SENSS Specialist Training: Forecasting: Methods, Evaluation, and Applications  

Instructor: Prof. Giovanni Urga (Bayes Business School)
Term: Autumn 2025

Module Outline and Aims

The course will provide an introduction to time series (and panel) methods for modelling and fore­casting economic and financial variables.  The course covers several theoretical and empirical topics in economics and financial econometrics providing a comprehensive presentation of the econometric methods applied to finance. Topics include: forecasting and forecast evaluation, estimation methods such as GMM and MLE, univariate and multivariate GARCH models, realised and stochastic volatility models, measurement techniques and tests for contagion, principal components and factor analysis, the use of OxMetrics/Autometrics in model selection in presence of a large number of regressors. The theory is illustrated in practice modelling of interest rates, asset prices and forex time series at several temporal frequencies.

Prerequisites

Students are expected to have a knowledge of statistics (descriptive and inference) and basic econometrics.

Software

Participants will use OxMetrics during the session. Instructions for installation will be provided in advance.

Content Outline

• Modelling and forecasting the conditional mean of financial time series.
• Modelling and forecasting the volatility of financial time series.
• Modelling and forecasting correlations and contagion.
• Hands-on modelling with real-world big data using OxMetrics/Autometrics.

Learning Objectives

By the end of the session, participants will be able to:

1. You will master statistical analysis tools to model and forecast economic and financial time series, volatility and correlations.
2. You will develop expertise in identifying and measuring contagion between markets.
3. You will gain practical experience with high-frequency data analysis and assessing the impact of market announcements.
4. You will apply advanced econometric techniques to real-world financial problems through case studies and simulations.

Main References

• Brockwell, P.J and R. A. Davis (2016), Introduction to time series and forecasting, Springer.
• Castle, J. L., Clements, M. P., and D. F. Hendry (2019), Forecasting an essential introduction, Yale University Press
• Diebold X. Francis (2024), Forecasting in economics, business, finance and beyond. Department of Economics, University of Pennsylvania, http://www.ssc.upenn.edu/~fdiebold/Textbooks.html
• Elliott, G. and A. Timmermann (2016) Forecasting in Economics and Finance. Annual Review of Financial Economics, 8, 81-110.
• Ghysels, E. and M. Marcellino (2018) Applied Economic Forecasting using Time Series Methods, Oxford University Press.
• Timmermann, A. (2018), “Forecasting Methods in Finance, Annual Review of Financial Economics, 10, 449-479.

Please ensure you meet the prerequisites before registering.

Sign up form: https://essex.eu.qualtrics.com/jfe/form/SV_7VAr6DaaGpubDNA

Please direct enquiries to: trainingmanager@senss-dtp.ac.uk