One of the most important questions facing decision-makers is: Why are forecasts from so many of today’s sophisticated economic models so often disappointing? The easy answer is, GIGO. “GIGO,” as everyone knows and Wikipedia explains, “is an acronym for Garbage In, Garbage Out. It is a fundamental computing and data science principle stating that the quality of an output is strictly determined by the quality of the input. If flawed, biased, or poor-quality data (garbage) is fed into a system, the resulting analysis or output will be equally flawed (garbage), regardless of how advanced the algorithm is.” So, bad stuff in, bad stuff out.   It is not a new idea.   Charles Babbage was using it in France a hundred years before computers existed. Its current popularity began in the middle of the 20th century when computer programmers and engineers used it to describe how flawless code can deliver bad results when it is fed bad data. But the failures of so many forecasting models use...