Across-Design Uncertainty in Short Pricing Panels: Evidence from Simulated Price Trajectories
Why standard price data can fool researchers about how uncertain their estimates really are
When researchers measure price changes from real-world data, they typically underestimate how much their estimates might vary simply because they only observe one possible sequence of price movements. A new analysis shows that this hidden uncertainty accounts for over 97% of estimation error in typical pricing datasets—but standard statistical techniques completely miss it. The solution: researchers need to actively design how price data gets collected rather than passively relying on whatever prices happen to be recorded.
Price data feeds into inflation estimates, wage negotiations, and policy decisions across economics and business. If researchers systematically underestimate the true range of uncertainty in their findings, they may report confident conclusions that are actually fragile. The paper's results suggest that better data design—ensuring prices move independently across different regions or products rather than moving together—could roughly double the reliability of these estimates.