In response to Nicholas Gruen’s suggestions for improving economic forecasts, I agree that there are valid criticisms of the current models used by central banks and governments. However, comparing these models to atmospheric models used in weather forecasting can lead to misunderstandings. Weather forecasting is a prime example of a “chaotic” system, relying on the interactions between precise physical variables. These models are based on the laws of physics and chemistry, with the challenge lying in accurately measuring and modeling the three-dimensional area of the atmosphere and specifying initial conditions. However, even with powerful supercomputers, weather forecasts can only accurately predict about 10 days ahead.
Economic models, including financial ones, can also exhibit characteristics of chaotic systems. Economics is considered a “soft” science, dealing with complex, non-linear, and adaptive systems. Various economic agents and their relationships, along with assumptions, simplifications, and parameterizations, make modeling difficult. Economic variables are often abstract concepts, poorly defined, challenging to measure, and subject to revisions over time. Despite these challenges, economic forecasts are still expected to provide accurate predictions over months or even years.
Overall, while improvements can be made to economic forecasting models, it is important to recognize the inherent differences between weather forecasting and economic forecasting.