Introductory description

The module aims to providing training on methods that aid decision making when managers face an uncertain future.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Topics covered are in four parts:
1- Decision Making when facing an uncertain future: the consumption-saving decision; the capital investment decision.
2 - How to compute probabilistic forecasts. What did we learn from weather forecasting? How to measure macroeconomic uncertainty using news-based indexes, stock market volatility and past forecasting errors.
3 – Methods for short-term forecasting, including product demand forecasting. How and when to add judgement to statistical forecasts.
4 – Methods for long-term forecasting. Probabilistic Forecasting, Scenario Analysis and Planning. Scenario analysis in the energy industry. OR Climate change forecasting (depending on resource availability)

Learning outcomes

By the end of the module, students should be able to:

• Measure macroeconomic uncertainty and understand its impact on decision making.
• Use probabilistic forecasts for decision making.
• Choose the forecasting method adequate to the problem at hand, including when to add judgment, use scenario analysis and/or use statistical methods.
• Compute short-term forecasts for product demand and macroeconomic variables, and use scenario analysis to aid long-term forecasting.
• Have improved skills in analysing time series data.

Indicative reading list

Silver, Nate (2013) The signal and the noise: the art and science of prediction. Penguin.
Hydman, R and Athanasopoulos, F. (2013) Forecasting: principles and practice.
Williamson, S. D. (2014) Macroeconomics. 5th edition. Pearson. (two chapters only).
Bloom, N. (2014) Fluctuations in Uncertainty. Journal of Economic Perspectives. 28:153-176.
Wright, G. and Goodwin, P. (2009) Decision making and planning under low levels of predictability: enhancing the scenario method. International Journal of Forecasting. 25: 813-825.
F. X. Diebold (2001) Elements of Forecasting. Thompson Learning
F. X. Diebold (2019) Econometric Data Science: A Predictive Modeling Approach
F. X. Diebold and G. Rudebusch (2021) Probabilistic Assessments of an Ice-Free Artic; Comparing Statistical and Climate Model Projections, Journal of Econometrics
Elliot, G. and A. Timmermann (2016) Economic Forecasting. Princeton University Press.

Subject specific skills

Use probabilistic forecasts for decision making.

Transferable skills

Write effectively. Perform well in team working. Present well their analysis for an audience.