The Binary Forecast

Economic variables are continuous — GDP growth, unemployment rate, yield spread. Recession prediction models feed these continuous inputs into increasingly sophisticated algorithms. More features, more nonlinearity, more parameters. The assumption: more information means better predictions.

The at-risk transformation inverts this. Instead of feeding the yield spread as a continuous number, binarize it: is the spread in the danger zone or not? Apply this to every predictor. The continuous landscape of economic indicators becomes a binary checklist: each variable is either at risk or not.

The result: simple linear models using binarized inputs become competitive with complex machine learning methods using continuous inputs. The gains are largest at recession onset — exactly when prediction matters most.

The structural insight is about where the information lives. A yield spread of -0.5% and a yield spread of -2.0% are both bad. The difference between them contains much less predictive information than the difference between +0.1% and -0.1%. The continuous value is dominated by noise in the interior of each regime and signal only at the boundary. Binarization throws away the noise and keeps the signal.

This is a specific instance of a general principle: when the relationship between predictor and outcome is threshold-dominated, continuous modeling wastes capacity learning the wrong function. The at-risk transformation doesn’t lose information — it discards the information that was confusing the model. Simplification as amplification. The binary question “is this variable in danger?” turns out to contain most of what the continuous variable knows about recessions.