We apply artificial neural networks (ANNs) to nowcast quarterly GDP growth for the U.S. economy. Using the monthly FRED-MD database, we compare the nowcasting performance of five different ANN architectures: the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the Elman recurrent neural network (RNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU). The empirical analysis presents results from two distinctively different evaluation periods. The first (2012:Q1 -- 2019:Q4) is characterized by balanced economic growth, while the second (2012:Q1 -- 2022:Q4) also includes periods of the COVID-19 recession. According to our results, longer input sequences result in more accurate nowcasts in periods of balanced economic growth. However, this effect ceases above a relatively low threshold value of around six quarters (eighteen months). During periods of economic turbulence (e.g., during the COVID-19 recession), longer input sequences do not help the models' predictive performance; instead, they seem to weaken their generalization capability. Combined results from the two evaluation periods indicate that architectural features enabling long-term memory do not result in more accurate nowcasts. Comparing network architectures, the 1D CNN has proved to be a highly suitable model for GDP nowcasting. The network has shown good nowcasting performance among the competitors during the first evaluation period and achieved the overall best accuracy during the second evaluation period. Consequently, first in the literature, we propose the application of the 1D CNN for economic nowcasting.

翻译：我们应用人工神经网络（ANNs）对美国经济季度GDP增长率进行即时预测。利用月度FRED-MD数据库，我们比较了五种不同ANN架构的即时预测性能：多层感知器（MLP）、一维卷积神经网络（1D CNN）、Elman循环神经网络（RNN）、长短期记忆网络（LSTM）和门控循环单元（GRU）。实证分析展示了来自两个截然不同评估期的结果。第一个评估期（2012年第一季度至2019年第四季度）以经济平稳增长为特征，而第二个评估期（2012年第一季度至2022年第四季度）则包含了COVID-19衰退时期。研究结果显示，在经济平稳增长时期，更长的输入序列能带来更准确的即时预测。然而，该效应在相对较低的阈值（约六个季度，即十八个月）以上便不再显著。在经济动荡时期（例如COVID-19衰退期间），较长的输入序列不仅无助于提升模型的预测性能，反而似乎削弱了其泛化能力。两个评估期的综合结果表明，具备长期记忆能力的架构特征并未带来更准确的即时预测。在架构比较中，1D CNN被证明是适用于GDP即时预测的高度适配模型。该网络在第一个评估期于各竞争模型中展现出良好的即时预测性能，并在第二个评估期取得了总体最佳精度。因此，作为文献中的首次尝试，我们提出将1D CNN应用于经济即时预测。