We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large‐scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in‐sample and out‐of‐sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.

中文翻译：

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使用动态因素和神经网络模型识别美国商业周期制度

我们使用动态因素和神经网络模型来识别美国商业周期的当前和过去状态（而不是未来）。第一步，我们通过使用移动平均滤波器来减少数据中的噪声。然后从包含 100 多个变量的大规模数据集中提取动态因素。在最后一步，这些动态因素被输入到神经网络模型中，用于预测商业周期制度。我们表明，我们提出的方法在不考虑历史数据可用性的情况下，在样本内和样本外非常准确地遵循了美国商业周期制度。我们的结果还表明，降噪是商业周期预测的重要一步。此外，使用伪实时和复古数据，