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Nonparametric estimation of ATE and QTE: an application of Fractile Graphical Analysis
Nonparametric estimators for average and quantile treatment effects are constructed using Fractile Graphical Analysis, under the identifying assumption that selection to treatment is based on observable characteristics. The proposed method has two-steps: first, the propensity score is estimated, and second, a blocking estimation procedure using this estimate is used to compute treatment effects. In both cases, the estimators are proved to be consistent. Monte Carlo results show a better performance than other procedures based on the propensity score. Finally, these estimators are applied to a job training dataset
Moment Restriction-based Econometric Methods: An Overview
Moment restriction-based econometric modelling is a broad class which includes the parametric, semiparametric and nonparametric approaches. Moments and conditional moments themselves are nonparametric quantities. If a model is specified in part up to some finite dimensional parameters, this will provide semiparametric estimates or tests. If we use the score to construct moment restrictions to estimate finite dimensional parameters, this yields maximum likelihood (ML) estimates. Semiparametric or nonparametric settings based on moment restrictions have been the main concern in the literature, and comprise the most important and interesting topics. The purpose of this special issue on “Moment Restriction-based Econometric Methods†is to highlight some areas in which novel econometric methods have contributed significantly to the analysis of moment restrictions, specifically asymptotic theory for nonparametric regression with spatial data, a control variate method for stationary processes, method of moments estimation and identifiability of semiparametric nonlinear errors-in-variables models, properties of the CUE estimator and a modification with moments, finite sample properties of alternative estimators of coefficients in a structural equation with many instruments, instrumental variable estimation in the presence of many moment conditions, estimation of conditional moment restrictions without assuming parameter identifiability in the implied unconditional moments, moment-based estimation of smooth transition regression models with endogenous variables, a consistent nonparametric test for nonlinear causality, and linear programming-based estimators in simple linear regression.robustness;testing;estimation;model misspecification;moment restrictions;parametric;semiparametric and nonparametric methods
Bootstrap-Based Inference for Cube Root Asymptotics
This paper proposes a valid bootstrap-based distributional approximation for
M-estimators exhibiting a Chernoff (1964)-type limiting distribution. For
estimators of this kind, the standard nonparametric bootstrap is inconsistent.
The method proposed herein is based on the nonparametric bootstrap, but
restores consistency by altering the shape of the criterion function defining
the estimator whose distribution we seek to approximate. This modification
leads to a generic and easy-to-implement resampling method for inference that
is conceptually distinct from other available distributional approximations. We
illustrate the applicability of our results with four examples in econometrics
and machine learning
Do debit cards increase household spending? Evidence from a semiparametric causal analysis of a survey
Motivated by recent findings in the field of consumer science, this paper
evaluates the causal effect of debit cards on household consumption using
population-based data from the Italy Survey on Household Income and Wealth
(SHIW). Within the Rubin Causal Model, we focus on the estimand of population
average treatment effect for the treated (PATT). We consider three existing
estimators, based on regression, mixed matching and regression, propensity
score weighting, and propose a new doubly-robust estimator. Semiparametric
specification based on power series for the potential outcomes and the
propensity score is adopted. Cross-validation is used to select the order of
the power series. We conduct a simulation study to compare the performance of
the estimators. The key assumptions, overlap and unconfoundedness, are
systematically assessed and validated in the application. Our empirical results
suggest statistically significant positive effects of debit cards on the
monthly household spending in Italy.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS784 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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