Reproduce “Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)” on OpenResearch
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About this paper
Daniel Hopp
The research evaluates Long Short-Term Memory (LSTM) artificial neural networks for economic nowcasting, demonstrating their general superiority in predictive accuracy over Dynamic Factor Models (DFMs) for global trade variables. LSTMs achieved statistically lower Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) across various data vintages and showed greater computational efficiency when handling numerous input features.
Key insights
- LSTMs generally outperform DFMs in predictive accuracy for economic nowcasting, yielding statistically significant lower errors across most target variables and data availability scenarios.
- LSTMs offer substantially greater computational efficiency with a large number of input features compared to DFMs, allowing for the integration of more diverse indicators without severe bottlenecks.
- The inherent stochasticity of LSTMs can be effectively mitigated by averaging predictions from an ensemble of independently trained networks, improving overall robustness.
Summary by alphaXiv.
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