3 citations · 11 across the 17 of their papers we have counts for
14 papers · 1 filter
Locally Robust Inference with Outcomes Predicted by Machine Learning
Juan Carlos Escanciano, Alejandro Puerta-Cuartas
Many parameters of interest in economics depend on outcomes generated by machine learning (ML).These generated outcomes are subsequently used in standard econometric estimation and…
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…
Machine Learning Debiasing with Conditional Moment Restrictions: An Application to LATE
Facundo Argañaraz, Juan Carlos Escanciano
Models with Conditional Moment Restrictions (CMRs) are popular in economics. These models involve finite and infinite dimensional parameters. The infinite dimensional components in…
Extending the Scope of Inference About Predictive Ability to Machine Learning Methods
Juan Carlos Escanciano, Ricardo Parra
The use of machine learning methods for predictive purposes has increased dramatically over the past two decades, but uncertainty quantification for predictive comparisons remains…
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…
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…
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…
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…
Regression Discontinuity Design with Multivalued Treatments
Carolina Caetano, Gregorio Caetano, Juan Carlos Escanciano
We study identification and estimation in the Regression Discontinuity Design (RDD) with a multivalued treatment variable. We also allow for the inclusion of covariates. We show th…
Uniform Rates for Kernel Estimators of Weakly Dependent Data
Juan Carlos Escanciano
This paper provides new uniform rate results for kernel estimators of absolutely regular stationary processes that are uniform in the bandwidth and in infinite-dimensional classes…
Irregular Identification of Structural Models with Nonparametric Unobserved Heterogeneity
Juan Carlos Escanciano
One of the most important empirical findings in microeconometrics is the pervasiveness of heterogeneity in economic behaviour (cf. Heckman 2001). This paper shows that cumulative d…
Quantile-Regression Inference With Adaptive Control of Size
Juan Carlos Escanciano, Chuan Goh
Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This paper develops a new estimate of the asympto…
Optimal Linear Instrumental Variables Approximations
Juan Carlos Escanciano, Wei Li
This paper studies the identification and estimation of the optimal linear approximation of a structural regression function. The parameter in the linear approximation is called th…