Optimal Transport Methods in Economics

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Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach (2021, background): “…since has been among the most active domains of mathematical analysis, with applications in various fields, from fluid mechanics to economics (see Galichon (2016)), learning, and statistics (Carlier et al. (2016); Panaretos and Zemel (2016, 2018); Álvarez et al. (2018) and del Barrio et al.…”
Orthogonal Estimation of Wasserstein Distances (2019, methodology): “, 1998) and economics (Galichon, 2016), and are increasingly used in machine learning (Arjovsky et al.”
Cupid’s Invisible Hand: Social Surplus and Identification in Matching Models (2015, neutral): “Then ε = Ze; and −G∗(µ) = min U0=0,U∈RY ∫ max y∈Y0 { (Ze)y + Uy } dP ( )− ∑ y∈Y µyUy (2.15) is the solution to the power diagram problem (see Galichon, 2016, Chapter 5).”
Vector quantile regression beyond correct specification (2016, methodology): “vex on [0,1]d such that ∇ϕ #µ= Law(Y), this map is by deﬁnition the vector quantile function of Y. We refer to the textbooks [15], [16] and [14] for a presentation of optimal transport theory, and to [7] for a survey of applications to economics. 4 2.2 Conditional vector quantiles Take a N-dimension”
Computational Optimal Transport (2018, background): “, 2016], and finds numerous applications in economics to interpret matching data [Galichon, 2016].”