LLMs show promise, but are 'not yet capable of providing credible explanations for their own predictions'
"Over the past year, central banks have had to answer difficult questions about our collective failure to foresee the persistence of inflation after the pandemic, which has in turn called into question the usefulness of our models," deputy managing director for economic policy and chief economist at the Monetary Authority, Edward S Robinson, declared in a Monday"Consequently, we may ask if economists should be paying more attention to recent advances in data analytics and...
Edwards then praised AI/ML modelling approaches for"their ability to let the data flexibly determine the functional form of the model," as that capability"potentially allows AI/ML models to capture non-linearities in economic dynamics in a way that mimics expert judgement." "The flexibility of this class of models is also a drawback: AI/ML models can be 'fragile' in that their output is often highly sensitive to the choice of model parameters or prompts provided," Edwards observed. Combined with opacity of output,"this flaw makes it difficult to parse the underlying drivers of the process being modelled."
Edwards therefore suggested the best role for current LLMs in central bank modelling toolkits"is to use them in satellite models that complement core structural models."
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