Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories

Abstract:Short observational pricing panels can contain many observations while offering only a small number of distinct price movements. This paper studies the inferential consequences of that distinction in a synthetic data-generating process calibrated to a sparse pricing regime. We separate uncertainty conditional on a realised price trajectory from variation in estimation error across alternative trajectories generated by the same pricing process. In the baseline simulations, the latter component accounts for 97.6% of the variance of estimation error for the gradient-boosted specification. Within-panel resampling procedures use the information of one realised trajectory and do not identify this across-design component. Three results organise the analysis. First, across-design dispersion is well described by the empirical relation sigma_hat approx 0.182 V^(-0.271), where V equals moves times magnitude squared. Second, adding regions sharing a common price path reduces outcome noise but does not create independent price trajectories; conversely, averaging across units with independent design-specific errors reduces dispersion at the standard square root rate. Third, a Paule-Mandel variance component estimated across independently priced units substantially increases empirical coverage in homogeneous simulations, from 0.469 to 0.931. The broader implication is a shift toward designing data-generating processes that create independent identifying variation rather than relying solely on fixed passive panels.

Machine Learning (cs.LG); Econometrics (econ.EM)

arXiv:2608.21334 [cs.LG]

(or arXiv:2608.21334v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.21334 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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