Research
Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories
arXiv:2608.21334v1 Announce Type: new Abstract: Short observational pricing panels can contain many observations while offering only a small number of distinct price movements. This paper studies the
arXiv:2608.21334v1 Announce Type: new 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.
Related
- DRIFT: Direct-Recursive Intervention-Conditioned Forecasting of ICU Physiological Trajectories
- Mapping High-Performance Regions in Battery Scheduling across Data Uncertainty, Battery Design, and Planning Horizons
- Random-Effects Algorithm for Random Objects in Metric Spaces
Source: arXiv cs.LG | 2026-08-24