Pedro Cadahia Delgado
arXiv 21 Aug 2026 · Machine Learning
arXiv:2608.21334 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cameron, A Colin and Gelbach, Jonah B and Miller, Douglas L (2011) Robust inference with multiway clustering | 0.644 | 2 | 2 | 100% |
| 2 | Chiang, Harold D and Kato, Kengo and Ma, Yukun and Sasaki, Yuya (2022) Multiway cluster robust double/debiased machine learning | 0.644 | 2 | 2 | 100% |
| 3 | Nakamura, Emi and Steinsson, Jón (2008) Five Facts about Prices: A Reevaluation of Menu Cost Models | 0.644 | 2 | 2 | 100% |
| 4 | Paule, Robert C and Mandel, John (1982) Consensus values and weighting factors | 0.644 | 2 | 2 | 100% |
| 5 | Abadie, Alberto and Athey, Susan and Imbens, Guido W. and Wooldridge… (2020) Sampling-Based versus Design-Based Uncertainty in Regression Analysis | 0.405 | 1 | 1 | 100% |
| 6 | Andrews, Isaiah and Stock, James H. and Sun, Liyang (2019) Weak instruments in IV regression: Theory and practice | 0.405 | 1 | 1 | 100% |
| 7 | Armstrong, Timothy B. and Kolesár, Michal (2020) Simple and Honest Confidence Intervals in Nonparametric Regression | 0.405 | 1 | 1 | 100% |
| 8 | Bijmolt, Tammo HA and Van Heerde, Harald J and Pieters, Rik GM (2005) New empirical generalizations on the determinants of price elasticity | 0.405 | 1 | 1 | 100% |
| 9 | Cameron, A Colin and Gelbach, Jonah B and Miller, Douglas L (2008) Bootstrap-based improvements for inference with clustered errors | 0.405 | 1 | 1 | 100% |
| 10 | Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations.