  A common problem when forecasting rare events, such as recessions, is limited
data availability. Recent advancements in deep learning and generative
adversarial networks (GANs) make it possible to produce high-fidelity synthetic
data in large quantities. This paper uses a model called DoppelGANger, a GAN
tailored to producing synthetic time series data, to generate synthetic
Treasury yield time series and associated recession indicators. It is then
shown that short-range forecasting performance for Treasury yields is improved
for models trained on synthetic data relative to models trained only on real
data. Finally, synthetic recession conditions are produced and used to train
classification models to predict the probability of a future recession. It is
shown that training models on synthetic recessions can improve a model's
ability to predict future recessions over a model trained only on real data.
