Economic predictions have always been highly variable and uncertain, and, for some reason, relied upon as if the future were a magical algorithm. Essentially, economists would make one fundamental mistake. They thought they were practicing a science. Data could be collected, inputted, and a predictive algorithm could be generated. Even Nobel Prize winners like Paul Samuelson believed that with enough data we could come to understand the economy and how it functioned.
This is nonsense. As Daniel Kahneman and Amos Tversky have shown us, human behavior and irrationality, combined with unpredictability and randomness (thank you Naseem Taleb) make this even a questionable social science. Using existing analysis and algorithms to reliably forecast is a fool’s errand, essential for someone’s tenure, and maybe even a Nobel Prize, but doesn’t add much that is useful. Some of the more laughable Nobel Prizes have been given to people who determined that markets were efficient. They are not. Economies can be predicted with useful data input. They cannot. A couple of inputs about inflation and the unemployment rate, and we know how to manage an economy. We can’t. That last one is the Philip’s Curve – true for a limited time and then it goes spectacularly wrong – a lot like most risk and market prediction models.