The inversion of the yield-curve has long held up as the single most prominent early-warning indicator of a looming recession in the United States.
Yet, recessions are arguably the result of a complex convolution of many economic variables. While such a setting strains the capabilities of orthodox models, machine-learning algorithms are supposed to excel in such environments. Still, classical probit and penalized regression models turn out to be resilient competitors.
Beyond providing a plain point-forecast and quantifying its uncertainty, I address the criticism of ML's limited interpretability. The results corroborate the standing of the yield-curve as the principal predictor of U.S. recessions, followed by labor- and stock-market indicators.
Bundling predictive ability and interpretability within a single package, I propose RecAE, a structural autoencoder-type architecture that leverages the predictive power of the yield-curve while conditioning its relation with the probability of an upcoming recession on the state of the economy. The RecAE identifies three consecutive regimes in which recession-probabilities have become increasingly sensitive to a steepening of the yield-curve, hinting at the U.S. economy undergoing several structural changes. The results advocate for carefully designed ML to be a valuable addition to the econometric toolbox.