Bootstrap Standard Error Estimates for Linear Regression

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...

Bootstrap Variance Estimates for Neural Networks Regression Models

where f is a non linear continuous function, ( ) dt t t x x , , 1 K = x is a vector of d non stochastic explanatory variables defined on a compact א⊂R, and { } t Z is a stationary noise process with zero mean. The function f in the model (1) can be approximated with a single hidden layer feed-forward neural network; Hornik et al. (1989) have shown that this class of non linear functions can app...

Bootstrap tests for the error distribution in linear and nonparametric regression models

In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the res...

Bootstrap Tests of Nonnested Linear Regression

L”-error estimates for linear elasticity problems

Two-dimensional linear elasticity problems are approximately solved by a mixed finite-element method based on Raviart-Thomas-Nedelec spaces; maximum-norm error estimates of optimal rate and almost optimal regularity are derived for the displacement field, and of optimal regularity and almost optimal rate for the stress tensor. The superconvergence of the approximate displacement field to the L2...