Explanation:
High-frequency payment data contains a vast amount of short-term variations and noise (such as holiday spending spikes or special events). AI models, particularly complex machine learning algorithms, can inadvertently overfit to this short-term noise rather than capturing the underlying true macroeconomic trends. This leads to overly volatile and potentially misleading estimates of economic activity. This scenario highlights the difficulty of filtering out the noise to extract the meaningful economic signal.
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Q.69 A central bank is using AI to analyze high-frequency payment data to nowcast economic activity. They notice that the AI model is highly sensitive to short-term fluctuations in consumer spending around holidays and special events, leading to overly volatile estimates of economic growth. What challenge related to AI-driven economic analysis is this scenario highlighting?
A
Difficulty in extracting meaningful signals from noisy data.
B
Over-reliance on high-frequency data leading to short-term biases.
C
Inadequate model calibration to account for seasonal variations.
D
Limited ability of AI models to capture structural changes in the economy.
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