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

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  • Hopp Daniel

    (UNCTAD E-9042, Palais des Nations, 1211 Geneva 10, Switzerland.)

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 well-suited 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 stochastic nature of 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 (Hopp 2021a) library was developed using PyTorch. The library is also available in R, MATLAB, and Julia.

Suggested Citation

  • Hopp Daniel, 2022. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Journal of Official Statistics, Sciendo, vol. 38(3), pages 847-873, September.
    • Handle: RePEc:vrs:offsta:v:38:y:2022:i:3:p:847-873:n:8
    DOI: 10.2478/jos-2022-0037
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    References listed on IDEAS

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