On economic forecasting and pure statistical forecasts vs. modeling/simulation forecasts
The goal of any predictive model is to capture as much signal as and as little noise as possible. Striking the right balance is not always and our ability to do so will be dictated by the strength of the theory quality and quantity of the data. In economic forecasting, the data is and the theory is weak, hence Armstrong's argument that "the more you make the model the worse the forecast gets."
In fact, up until about thirty years ago, purely statistical models were the primary way that the weather service forecasted hurricane trajectories.
Such techniques, however, are subject to diminishing returns. Hurricanes are not exactly rare, but severe storms hit the United States perhaps once every year on average. Whenever you have a large number of candidate variables applied to a rarely occurring phenomenon, there is the risk of overfitting your model and mistaking the noise in the past data for a signal.
This second type of model essentially creates a simulation of the physical mechanics of some portion of the universe. It takes much more work to build than a purely statistical method and requires a more solid understanding of the root causes of the phenomenon.
