In the investment management business, it’s standard practice to come up with macro forecasts and bet clients’ money on them. And these days it seems as if investors hang on forecasters’ every word. While I’ve long expressed my disregard for this, I believe it’s now important to consider why making helpful macro forecasts is so difficult.
Forecasters have no choice but to base their judgments on models, be they complex or informal, mathematical or intuitive. Models, by definition, consist of assumptions: “If A happens, then B will happen.” In other words, relationships and responses. When I think about modelling an economy, my first reaction is to consider how incredibly complicated this task is.
To predict the path of the US economy, you have to forecast the behaviour of hundreds of millions of consumers, plus millions of workers, producers and intermediaries. A real simulation would therefore have to deal with billions of interactions, including those with suppliers, customers and other market participants around the globe.