An inverse framework uses quantile-normalized orderbook data and machine learning models to predict allocative efficiency in double auctions from bids, asks, and prices without knowing induced values.
Arestructuralestimatesofauctionmodelsreasonable? evidence from experimental data.Journal of Political economy, 113(4):703–741
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An `Inverse' Experimental Framework to Estimate Market Efficiency
An inverse framework uses quantile-normalized orderbook data and machine learning models to predict allocative efficiency in double auctions from bids, asks, and prices without knowing induced values.