Calibrated Percentile Double Bootstrap For Robust Linear Regression Inference

Calibrated Percentile Double Bootstrap for Robust Linear Regression Inference

Calibrated Very Robust Regression

The behaviour of algorithms for very robust regression depends on the distance between the regression data and the outliers. We introduce a parameter λ that defines a parametric path in the space of models and enables us to study, in a systematic way, the properties of estimators as the groups of data move from being far apart to close together. We examine, as a function of λ, the variance and ...

Cluster-robust Bootstrap Inference in Quantile Regression Models

In this paper I develop a wild bootstrap procedure for cluster-robust inference in linear quantile regression models. I show that the bootstrap leads to asymptotically valid inference on the entire quantile regression process in a setting with a large number of small, heterogeneous clusters and provides consistent estimates of the asymptotic covariance function of that process. The proposed boo...

Bootstrap Standard Error Estimates for Linear Regression

Standard errors of parameter estimates are widely used in empirical work. The bootstrap often can provide a convenient means of estimating standard errors. The conditions under which bootstrap standard error estimates are theoretically justified have not received much attention, however. This article establishes conditions for the consistency of the moving blocks bootstrap estimators of the var...

Regression Analysis with Many Specifications: A Bootstrap Method for Robust Inference

We consider a set of linear regression models that differ in their choice of regressors, and derive a method for inference that controls for the set of models under investigation. The method is based around an estimate of the distribution for a class of statistics, which can depend on two or more models. An example is the largest R2 over a set of regression models. The distribution will typical...