  Long short-term memory (LSTM) and gated recurrent unit (GRU) are used to
model US recessions from 1967 to 2021. Their predictive performances are
compared to those of the traditional linear models. The out-of-sample
performance suggests the application of LSTM and GRU in recession forecasting,
especially for longer-term forecasts. The Shapley additive explanations (SHAP)
method is applied to both groups of models. The SHAP-based different weight
assignments imply the capability of these types of neural networks to capture
the business cycle asymmetries and nonlinearities. The SHAP method delivers key
recession indicators, such as the S&P 500 index for short-term forecasting up
to 3 months and the term spread for longer-term forecasting up to 12 months.
These findings are robust against other interpretation methods, such as the
local interpretable model-agnostic explanations (LIME) and the marginal
effects.
