System Dynamics is Key to Success in Economic Forecasting

Is the worst over? Is now the right time to jump back into the stock market? Economic models will fail to tell us where our economy goes from here unless they incorporate system dynamics.

Traditionally economic models have consisted of a number of equations that logically explain how the various sectors of the economy interact with one another. The money supply affects interest rates which are inversely related to bond prices. Wages and salaries affect consumer demand which affects output and, ultimately, employment and inflation.

To use these models for forecasting, they must allow past values to predict future values. This means introducing lagged variables into the models. This works fine for describing the past where the lagged values are all known such as when you want to predict just one period (say, a month) ahead. But what if you want to forecast the state of the economy many periods (months) in the future?

The problem is that the future values of the lagged variables are generally unknown. The "solution" that economists have come up with is to use so-called "time series" models which express the value of a variable such as stock prices as a function of its past values. In other words, you get next period's predicted values on the basis of last period's predicted values. This quickly compounds prediction errors. More importantly, the time series models lack much of the intuitive, logical relationships of the traditional economic models.

The real solution to this problem has been largely ignored by economists. Engineers at MIT and elsewhere have developed models that are much better equipped to deal with the interrelationships involved in forecasting. Their "system dynamics" models are basically systems of differential equations with positive and negative feedback loops. These models allow for both the traditional logical and intuitive economic relationships and an effective way of forecasting their values.