Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

Daniel Hopp

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arXiv · 2106.08901

Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)

Daniel Hopp

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Abstract

Artificial neural networks (ANNs) have been the catalyst to numerous advances in a variety of fields and disciplines in recent years. Their impact on economics, however, has been comparatively muted. One type of ANN, the long short-term memory network (LSTM), is particularly wellsuited to deal with economic time-series. Here, the architecture's performance and characteristics are evaluated in comparison with the dynamic factor model (DFM), currently a popular choice in the field of economic nowcasting. LSTMs are found to produce superior results to DFMs in the nowcasting of three separate variables; global merchandise export values and volumes, and global services exports. Further advantages include their ability to handle large numbers of input features in a variety of time frequencies. A disadvantage is the inability to ascribe contributions of input features to model outputs, common to all ANNs. In order to facilitate continued applied research of the methodology by avoiding the need for any knowledge of deep-learning libraries, an accompanying Python library was developed using PyTorch, https://pypi.org/project/nowcast-lstm/.

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Daniel Hopp. 2021-06-15. Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM). https://arxiv.org/abs/2106.08901

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