Sensitivity Analysis in Semiparametric Likelihood Models

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An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? (2020, background): “And practitioners may well benefit from robustifying their analysis (Mosteller and Tukey, 1977; Hansen and Sargent, 2008; Chen et al., 2011) even if they pass our check.”
Discrete games with flexible information structures: an application to local grocery markets (2014, unknown): “1 Electronic copy available at: http://ssrn.com/abstract=1859643”
Sensitivity analysis using approximate moment condition models (2018, methodology): “In the context of the moment condition model, such estimators have been recently studied in Liao (2013), Cheng and Liao (2015), and DiTraglia (2016).”
Minimizing Sensitivity to Model Misspecification (2018, methodology): “From (21) we have(12) h (y, x, z) = (y − xβ0)xΣ X c − (y − xβ0) [ (x− Cz)− ΣV Σ−1 X x ]′ [( ΣV − ΣV Σ−1 X ΣV ) + ( n)−1σ2I ]−1 ΣV Σ −1 X c.”
Counterfactual Sensitivity and Robustness (2019, background): “2See, for example, work on partially identified semiparametric models by Chen, Tamer, and Torgovitsky (2011) which is motivated partly by questions of sensitivity with respect to various modeling assumptions.”
Monte Carlo Confidence Sets for Identified Sets (2016, methodology): “This paper’s MC approach could be a useful alternative to the sieve MLE bootstrap inference on partially-identified likelihood models in Chen, Tamer, and Torgovitsky (2011).”