Bootstrapping General Empirical Measures

Bootstrapping general empirical measures

It is proved that the bootstrapped central limit theorem for empirical processes indexed by a class of functions F and based on a probability measure P holds a.s. if and only if F CLT (P ) and ∫ F dP < ∞, where F = supf F |f | and it holds in probability if and only if F ∈ CLT (P ). Thus, for a large class of statistics, no local uniformity of the CLT (about P ) is needed for the bootstrap to w...

Bootstrapping General First Order Autoregression

In this paper we consider general rst order autoregression, including the stationary, the explosive and the unstable case. It is well-known in the literature that the usual bootstrap method for the least squares parameter estimator is asymptotically consistent for the stationary and the explosive case, but does not work in the unstable case, where the parameter value is equal to + 1 or {1. We p...

Bootstrapping an Econometric Model: Some Empirical Results

The bootstrap, like the jackknife, is a technique for estimating standard errors. The idea is to usc Monte Carlo simulation, based on a non-parametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric model describing the demand for capital, labor, energy, and materials. The model is fitted by three-stage least squares. In sharp contrast with previou...

Empirical Bayes Estimators with Uncertainty Measures for NEF-QVF Populations

The paper proposes empirical Bayes (EB) estimators for simultaneous estimation of means in the natural exponential family (NEF) with quadratic variance functions (QVF) models. Morris (1982, 1983a) characterized the NEF-QVF distributions which include among others the binomial, Poisson and normal distributions. In addition to the EB estimators, we provide approximations to the MSE’s of t...

Bootstrapping Confidence Intervals For Robust Measures Of Association