Econometric forecasting of climate change

Book section : Chapter

Extreme weather events occurring around the world are a daily reminder that our climate is rapidly changing due to human activity. We need accurate forecasts of the climate but human behavior is non-stationary from both stochastic trends and location shifts leading to forecasts that are uncertain and prone to failure. Forecast success hinges on the ability to handle unanticipated shifts as climate change is characterised by ‘the change in the change’. Empirical modeling should consider the effects of historical structural breaks to avoid distortions in parameter estimates and the resulting forecasts. The chapter explains why it is important to identify and model location shifts and how doing so improves the verisimilitude of the model and its forecasts. Using indicator saturation estimators to capture in-sample shifts, improved econometric models and their forecasts can be achieved, as demonstrated within a system of four of the key climate variables.

Authors: Castle, JL; Hendry, DF; Miller, JI

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Edward Elgar Publishing
• Host title: Handbook of Research Methods and Applications in Macroeconomic Forecasting
• Pages: 361-395
• Chapter number: 14
• Series: Handbooks of Research Methods and Applications
• Place of publication: Cheltenham
• Publication date: 2024-11-26
• Acceptance date: 2024-01-07
• Edition: 1
• DOI: 10.4337/9781035310050.00018
• EISBN: 9781035310050
• ISBN: 9781035310043
• Language: English
• Keywords: density forecasts; Bayesian vector autoregressions; machine learning; Big Data; macroeconomic forecasting
• Subtype: Chapter