Averaging Quantiles, Variance Shrinkage, and Overconfidence
Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically.
Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically. Quantile averaging is equivalent to taking the harmonic mean of densities evaluated at quantile points. A variance shrinkage law is established between equal and harmonic weighting. Data from 49 post‐2006 studies are extended to include harmonic weighting in addition to equal and performance‐based weighting. It emerges that harmonic weighting has the highest average information and degraded statistical accuracy. The hypothesis that the quantile average is statistically accurate would be rejected at the 5% level in 28 studies and at the 0.1% level in 15 studies. For performance weighting, these numbers are 3 and 1, for equal weighting 2 and 1.
Working Paper — Feb 28, 2024
Spending and Pricing to Deter Arbitrage
This working paper presents examples of arbitrage deterrence from the pharmaceutical, chemical, and auto industries and uses them to generalize several models of arbitrage deterrence.
Common Resources — Feb 22, 2024
Modeling Deep Decarbonization in the Industrial Sector: Opportunities and Challenges for Modelers and Policymakers
Decarbonizing the industrial sector is a growing priority. To help produce models with high-quality data that inform policy, transparency and dialogue should be fostered among modeling colleagues, policymakers, and the modeling community.
Media Highlight — Nov 10, 2023
E&E News: “White House Overhaul Paves Way for Stricter Regulations”
RFF President and CEO Richard G. Newell comments on the White House's revised method for agencies to weigh regulatory costs and benefits.