We’ve come a long way. Not long ago, an economist entering into a consulting contract with the federal government might have to explain to the human resource department that economic “model building” didn’t involve balsa wood airplanes or toy boats.
Today, as a recent newspaper commentary headline pointed out, “Models Will Run the World.” It is a recognition that both industrial producers and marketing firms like Amazon owe their success to mathematical modeling of their operations. And the growth of models is just getting started.
Many of these models are quite complex, reflecting the world we live in. The economic model developed and used by the New York Federal Reserve Bank to support monetary policy decisions, for example, combines so many variables that wrangling them into a coherent picture would be unthinkable without today’s powerful computer assistance.
The Federal Reserve’s model is called “DSGE” — for Dynamic Stochastic General Equilibrium — and each word represents a key characteristic of how it reflects the movements of the U.S. economy.
The model works very well, but sometimes it misses changes in the economy that gum up the forecasts. Researchers are hard at work to diagnose and fix the cause of the problem, but a good guess is that it might involve a mix of expectations and anecdotal evidence.
The use of anecdotal evidence is of particular significance in monetary policy decision-making. Alan Greenspan served as chairman of the Federal Reserve Board from 1987 to 2006. As his term progressed he because increasingly concerned about the data he
Article source: https://www.heraldnet.com/business/even-with-ai-economic-forecasting-is-risky/