The neural network approach, while powerful in estimating complex relationships in trade data, has several limitations regarding interpretability and extrapolation:
Interpretability: Neural networks are often considered "black boxes," meaning that while they can produce accurate predictions, understanding the underlying mechanisms driving these predictions can be challenging. The lack of transparency in how the model arrives at its cost estimates ( C ) can hinder policymakers and economists from deriving actionable insights or justifying decisions based on the model's outputs.
Overfitting: There is a risk of overfitting the model to the training data, especially if the dataset is not sufficiently diverse or representative of all possible trade scenarios. This can lead to a model that performs well on historical data but fails to generalize to new situations, resulting in inaccurate predictions when faced with unseen data.
Extrapolation Limitations: The neural network's ability to extrapolate to new scenarios is contingent on the training data's breadth and diversity. If the model has not encountered specific trade patterns, policy changes, or economic conditions during training, it may struggle to provide reliable estimates in those contexts. For instance, sudden geopolitical events or unprecedented economic crises may not be adequately captured, leading to significant prediction errors.
Dependence on Data Quality: The performance of the neural network is heavily reliant on the quality and completeness of the input data. Missing or inaccurate data entries can propagate through the model, resulting in skewed estimates of trade costs and flows. This is particularly relevant in global trade, where data discrepancies between exporters and importers can lead to significant challenges.
Complexity of Relationships: While neural networks can capture non-linear relationships, they may not effectively model the intricate interactions between various factors influencing trade, such as cultural ties, historical relationships, and informal trade practices. These qualitative aspects are often difficult to quantify and may be overlooked in a purely data-driven approach.
Addressing these limitations requires ongoing research into model interpretability, the development of hybrid models that combine neural networks with more interpretable methods, and the incorporation of expert knowledge to enhance the robustness and applicability of the findings.