(The NWS gets by on just $900 million per year28—about $3 per U.S. citizen—even though weather has direct effects on some 20 percent of the nation’s economy.
What happens in systems with noisy data and underdeveloped theory—like earthquake prediction and parts of economics and political science—is a two-step process. First, people start to mistake the noise for a signal. Second, this noise pollutes journals, blogs, and news accounts with false alarms, undermining good science and setting back our ability to understand how the system really works.
Second, weather forecasts are subject to uncertain initial conditions. The probabilistic expression of weather forecasts (“there’s a 70 percent chance of rain”) arises not because there is any inherent randomness in the weather. Rather, the problem is that meteorologists assume they have imprecise measurements of what the initial conditions were like, and weather patterns (because they are subject to chaos theory) are extremely sensitive to changes in the initial conditions. In economic forecasting, likewise, the quality of the initial data is frequently quite poor.
This kind of statement is becoming more common in the age of Big Data.56 Who needs theory when you have so much information? But this is categorically the wrong attitude to take toward forecasting, especially in a field like economics where the data is so noisy. Statistical inferences are much stronger when backed up by theory or at least some deeper thinking about their root causes. There were certainly reasons for economic pessimism in September 201157—for instance, the unfolding debt crisis in Europe—but ECRI wasn’t looking at those. Instead, it had a random soup of variables that mistook...