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econ.EM2023
Robust Minimum Distance Inference in Structural Models
Joan Alegre, Juan Carlos Escanciano
This paper proposes minimum distance inference for a structural parameter of interest, which is robust to the lack of identification of other structural nuisance parameters. Some c…
econ.EM2023★ 1 cited
When Is Neyman-Orthogonal Inference Feasible? Existence and Relevance
Facundo Argañaraz, Juan Carlos Escanciano
Neyman-orthogonal moments underpin debiased and double machine learning, yet their existence and informativeness are typically presumed. We ask three questions: when do nontrivial…
econ.EM2023
Automatic Locally Robust GMM with Machine-Learning-Generated Regressors
Juan Carlos Escanciano, Telmo Pérez-Izquierdo
Machine-learning (ML) methods now routinely generate regressors used in subsequent econometric analyses, for example, estimated propensity scores, control-function residuals, imput…