• Corpus ID: 34119537

Bootstrap-Based Inference for Cube Root Consistent Estimators

  title={Bootstrap-Based Inference for Cube Root Consistent Estimators},
  author={M. D. Cattaneo and Michael Jansson and Kenichi Nagasawa},
  journal={arXiv: Statistics Theory},
This note proposes a consistent bootstrap-based distributional approximation for cube root consistent estimators such as the maximum score estimator of Manski (1975) and the isotonic density estimator of Grenander (1956). In both cases, the standard nonparametric bootstrap is known to be inconsistent. Our method restores consistency of the nonparametric bootstrap by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification… 

Tables from this paper

The Numerical Bootstrap

This paper proposes a numerical bootstrap method that is consistent in many cases where the standard bootstrap is known to fail and where the $m$-out-of-$n$ bootstrap and subsampling have been the

Rate-Adaptive Bootstrap for Possibly Misspecified GMM ∗

We consider inference for possibly misspecified GMM models based on possibly nonsmooth moment conditions. While it is well known that misspecified GMM estimators with smooth moments remain √ n

Finite Sample Inference for the Maximum Score Estimand

We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Manski (1975, 1985).

Bootstrap Methods in Econometrics

  • J. Horowitz
  • Economics, Mathematics
    Annual Review of Economics
  • 2019
The bootstrap is a method for estimating the distribution of an estimator or test statistic by resampling one's data or a model estimated from the data. Under conditions that hold in a wide variety

Inference Under Random Limit Bootstrap Measures

Asymptotic bootstrap validity is usually understood as consistency of the distribution of a bootstrap statistic, conditional on the data, for the unconditional limit distribution of a statistic of

Isotonic Regression Discontinuity Designs

In isotonic regression discontinuity designs, the average outcome and the treatment assignment probability are monotone in the running variable. We introduce novel nonparametric estimators for sharp

Semiparametric Estimation of Dynamic Binary Choice Panel Data Models

We propose a new approach to the semiparametric analysis of panel data binary choice models with fixed effects and dynamics (lagged dependent variables). The model we consider has the same random

Uniform inference for bounds on the distribution and quantile functions of treatment effects in randomized experiments

This paper develops a novel approach to uniform inference for functions that bound the distribution and quantile functions of heterogeneous treatment effects in randomized experiments when only

Statistical tests for equal predictive ability across multiple forecasting methods

A multivariate generalization of the Giacomini-White tests for equal conditional predictive ability is developed, applicable to a mixture of nested and non-nested models, and allow for misspecification of the forecasting model as well as non-stationarity of the data.

Binarization for panel models with fixed effects

In nonlinear panel models with fixed effects and fixed-T, the incidental parameter problem poses identification difficulties for structural parameters and partial effects. Existing solutions are



The Numerical Delta Method and Bootstrap

A numerical bootstrap method is proposed that provides asymptotically valid inference even for parameters that are not known to be directionally differentiable, and can consistently estimate the limiting distribution in many cases where the conventional bootstrap is known to fail.

On the bootstrap in cube root asymptotics

The authors study the application of the bootstrap to a class of estimators which converge at a nonstandard rate to a nonstandard distribution. They provide a theoretical framework to study its

A consistent bootstrap procedure for the maximum score estimator

Inconsistency of bootstrap: The Grenander estimator

In this paper, we investigate the (in)-consistency of different bootstrap methods for constructing confidence intervals in the class of estimators that converge at rate $n^{1/3}$. The Grenander

Local M-estimation with discontinuous criterion for dependent and limited observations

This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume predictive region,

Simple Local Polynomial Density Estimators

An intuitive and easy-to-implement nonparametric density estimator based on local polynomial techniques that is fully boundary adaptive and automatic, but does not require prebinning or any other transformation of the data is introduced.

Large Sample Confidence Regions Based on Subsamples under Minimal Assumptions

In this article, the construction of confidence regions by approximating the sampling distribution of some statistic is studied. The true sampling distribution is estimated by an appropriate

Inference on Directionally Differentiable Functions

This paper studies an asymptotic framework for conducting inference on parameters of the form ( 0), where is a known directionally dierentiable function and 0 is estimated by ^ n. In these settings,