  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/.
