Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions
نویسندگان
چکیده
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step, but retain a fully nonparametric specification in the first step. Such estimators exist in many economic applications, including a wide range of missing data and treatment effect models. We show that these estimators are √ n-consistent and asymptotically normal under weaker than usual conditions on the accuracy of the first stage estimates, have smaller first order bias and second order variance, and that their finite-sample distribution can be approximated more accurately by classical first order asymptotics. We argue that because of these refinements our estimators are useful in many settings where semiparametric estimation and inference are traditionally believed to be unreliable. We also provide some simulation evidence to illustrate the practical relevance of our approach. JEL Classification: C14, C21, C31, C51
منابع مشابه
Semiparametric Two-Step Estimation Using Doubly Robust Moment Conditions
We study semiparametric two-step estimators which have the same structure as parametric doubly robust estimators in their second step, but retain a fully nonparametric specification in the first step. Such estimators exist in many economic applications, including a wide range of missing data and treatment effect models, partially linear regression models, models for nonparametric policy analysi...
متن کاملDoubly Robust Causal Inference With Complex Parameters
Semiparametric doubly robust methods for causal inference help protect against bias due to model misspecification, while also reducing sensitivity to the curse of dimensionality (e.g., when high-dimensional covariate adjustment is necessary). However, doubly robust methods have not yet been developed in numerous important settings. In particular, standard semiparametric theory mostly only consi...
متن کاملDiscrete Choice Models for Nonmonotone Nonignorable Missing Data: Identification and Inference
Nonmonotone missing data arise routinely in empirical studies of social and health sciences, and when ignored, can induce selection bias and loss of efficiency. In practice, it is common to account for nonresponse under a missing-at-random assumption which although convenient, is rarely appropriate when nonresponse is nonmonotone. Likelihood and Bayesian missing data methodologies often require...
متن کاملCross-Fitting and Fast Remainder Rates for Semiparametric Estimation
There are many interesting and widely used estimators of a functional with finite semiparametric variance bound that depend on nonparametric estimators of nuisance functions. We use cross-fitting (i.e. sample splitting) to construct novel estimators with fast remainder rates. We give cross-fit doubly robust estimators that use separate subsamples to estimate different nuisance functions. We obt...
متن کاملCp criterion for semiparametric approach in causal inference
For marginal structural models, which recently play an important role in causal inference, we consider a model selection problem in the framework of a semiparametric approach using inverse-probability-weighted estimation or doubly robust estimation. In this framework, the modeling target is a potential outcome which may be a missing value, and so we cannot apply the AIC nor its extended version...
متن کامل