<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Artificial neural networks (ANNs) have been the catalyst to numerous advances in a variety of fields and disciplines in recent years. Their impact on...

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

Data publikacji: 12 wrz 2022

Zakres stron: 847 - 873

Otrzymano: 01 lip 2021

Przyjęty: 01 lut 2022

DOI: https://doi.org/10.2478/jos-2022-0037

Słowa kluczowe
Forecasting, machine learning, python

© 2022 Daniel Hopp, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Data publikacji: 12 wrz 2022

Zakres stron: 847 - 873

Otrzymano: 01 lip 2021

Przyjęty: 01 lut 2022

DOI: https://doi.org/10.2478/jos-2022-0037

Słowa kluczoweForecasting, machine learning, python

© 2022 Daniel Hopp, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Słowa kluczowe
Forecasting, machine learning, python