Robustness, Infinitesimal Neighborhoods, and Moment Restrictions

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Counterfactual Sensitivity and Robustness (2019, background): “In particular, Kitamura, Otsu, and Evdokimov (2013) and Bonhomme and Weidner (2018) are notable for their use of local neighborhoods characterized by statistical divergence.”
Measuring the Sensitivity of Parameter Estimates to Sample Statistics (2014, background): “10Kitamura et al. (2013) discuss robust estimation under related forms of local model misspecification.”
Sensitivity analysis using approximate moment condition models (2018, background): “As shown by Leeb and Pötscher (2006), c̃ cannot be consistently estimated in this setting, and any such procedure must adjust the resulting CI for the uncertainty in the estimate if coverage is to be maintained.”
Measuring the Sensitivity of Parameter Estimates to Estimation Moments (2017, methodology): “Our approach is related to influence function calculations for determining the distribution of estimators (Huber and Ronchetti 2009), and is particularly close to the large literature on moment estimators under local misspecification (for example Newey 1985; Berkowitz et al. 2008; Guggenberger 2012;”
Minimizing Sensitivity to Model Misspecification (2018, unknown): “We focus on asymptotically linear generalized method-of-moments (GMM) estimators of δθ0 that satisfy X 1 n − 1 bδ = δθ(η) + a(η)0 Ψ(Yi, θ(η)) + oP ( 21 ) + oP (n 2 ), (49) n i=1 for an η-specific parameter vector a(η).”