Shape-Constrained Density Estimation via Optimal Transport
Ryan Cumings-Menon

TL;DR
This paper introduces a novel shape-constrained density estimator using optimal transport, allowing for flexible regularity conditions beyond log-concavity, with applications in economic model estimation and auction cost analysis.
Contribution
It formulates a general shape-constrained density estimator via optimal transport, providing theoretical properties and a hypothesis test, extending beyond log-concavity constraints.
Findings
Estimator is consistent and asymptotically normal.
Convexity of the optimization problem is established.
Test for shape constraints performs well in applications.
Abstract
Constraining the maximum likelihood density estimator to satisfy a sufficiently strong constraint, concavity being a common example, has the effect of restoring consistency without requiring additional parameters. Since many results in economics require densities to satisfy a regularity condition, these estimators are also attractive for the structural estimation of economic models. In all of the examples of regularity conditions provided by Bagnoli and Bergstrom (2005) and Ewerhart (2013), concavity is sufficient to ensure that the density satisfies the required conditions. However, in many cases concavity is far from necessary, and it has the unfortunate side effect of ruling out sub-exponential tail behavior. In this paper, we use optimal transport to formulate a shape constrained density estimator. We initially describe the estimator using a concavity…
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Taxonomy
TopicsAuction Theory and Applications · Economic theories and models · Economic and Environmental Valuation
