– How did you actually implement the “theoretical grounding” from econometrics when feeding features engineered through ML into models like VAR? Was there a risk of theory being diluted by data-driven selection?
– The paper mentions interpretable ML techniques but doesn’t give examples; did you use SHAP, LIME, or something else in your case studies to explain the hybrid model outputs?
– When you combined ARIMA with neural networks, how did you handle the differing assumptions about stationarity and noise between the two modeling approaches?
– The 17% error reduction in GDP forecasting is compelling; was this consistent across different economies or time periods, or was it specific to a particular dataset?
– For the financial market case, you incorporated social media sentiment. How did you address the possibility of sentiment data introducing short-term noise rather than meaningful signal?
– You note that hybrid models are computationally expensive. Did you explore any specific optimizations or model compression techniques to make them more practical for real-time policy use?
– The ethical and bias discussion feels somewhat general. In your retail demand case, did you encounter any demographic or regional biases in the data, and if so, how were they corrected?
– The framework proposes merging structured econometric data with unstructured analytics data; could you walk through a concrete example of how you aligned these different data types in one of your case studies?
– Many of your citations are very recent (2024, 2023). How do you see this field evolving in the next few years, and what parts of your integration framework might become obsolete as ML methods advance?
– In the policy implications section, you mention dynamic updating of models. How would you recommend policymakers validate or trust a model that continuously retrains on new, possibly non-stationary data?
