2 citations · 2 across the 3 of their papers we have counts for
8 papers
Locally Robust Kernel Specification Tests for Conditional Moment Restrictions
Juan Carlos Escanciano
We develop kernel-based specification tests for semiparametric conditional moment models with high-dimensional nuisance parameters, extending existing conditional moment tests---wh…
Debiased Machine Learning U-statistics
Juan Carlos Escanciano, Joël Robert Terschuur
We propose a method to debias estimators based on U-statistics with machine-learning (ML) first steps. Standard plug-in estimators often suffer from regularization and model-select…
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…
Goodness-of-Fit Tests for Censored and Truncated Data: Maximum Mean Discrepancy Over Regular Functionals
Juan Carlos Escanciano, Jacobo de Uña-Ãlvarez
We develop a systematic, omnibus approach to goodness-of-fit testing for parametric distributional models when the variable of interest is only partially observed due to censoring…
Identification and Estimation in Fuzzy Regression Discontinuity Designs with Covariates
Carolina Caetano, Gregorio Caetano, Juan Carlos Escanciano
We study fuzzy regression discontinuity designs with covariates and characterize the weighted averages of conditional local average treatment effects (WLATEs) that are point identi…
Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models
Facundo Argañaraz, Juan Carlos Escanciano
Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedur…