Estimation of Kullback-Leibler information is a crucial part of deriving a statistical model selection procedure which, like AIC, is based on the likelihood principle. To discriminate between nested models, we have to estimate KullbackLeibler information up to the order of a constant, while Kullback-Leibler information itself is of the order of the number of observations. A correction term empl...

I propose a nonparametric iid bootstrap procedure for the empirical likelihood (EL), the exponential tilting (ET), and the exponentially tilted empirical likelihood (ETEL) estimators. The proposed bootstrap achieves sharp asymptotic refinements for t tests and confidence intervals based on such estimators. Furthermore, my bootstrap is robust to possible model misspecification, i.e., it achieves...

When time series data contain a periodic/seasonal component, the usual block bootstrap procedures are not directly applicable. We propose a modification of the block bootstrap—the Generalized Seasonal Block Bootstrap (GSBB)—and show its asymptotic consistency without undue restrictions on the relative size of the period and block size. Notably, it is exactly such restrictions that limit the app...

1 Abstract We will develop conndence intervals for linear regression coeecients when the para-metric model is violated by the presence of a fraction of outliers and high leverage data points. Our method will be based on a robustiied bootstrap technique. Unlike the classical bootstrap, our robust bootstrap does not produce unduly heavy tails or extreme re-sampled statistics when the original sam...

The e¢ cient bootstrap methodology is developed for overidenti…ed moment conditions models with weakly dependent observation. The resulting bootstrap procedure is shown to be asymptotically valid and can be used to approximate the distributions of t-statistics, J statistic for overidentifying restrictions, and Wald, Lagrange multiplier and distance statistics for nonlinear hypotheses. The asymp...

This paper is devoted to thoroughly investigating how to bootstrap the ROC curve, a widely used visual tool for evaluating the accuracy of test/scoring statistics s(X) in the bipartite setup. The issue of confidence bands for the ROC curve is considered and a resampling procedure based on a smooth version of the empirical distribution called the ”smoothed bootstrap” is introduced. Theoretical a...

In this paper, we propose nonparametric estimators of sharp bounds on the distribution of treatment e¤ects of a binary treatment and establish their asymptotic distributions. We note the possible failure of the standard bootstrap with the same sample size and apply the fewer-than-n bootstrap to making inferences on these bounds. The …nite sample performances of the con…dence intervals for the b...

This paper assesses the finite sample refinements of the block bootstrap and the Non-Parametric Bootstrap for conditional moment models. The study recononsiders inference in the generalized method of moments estimation of the consumption asset pricing model of Singleton (1986). These dependent bootstrap resampling schemes are proposed as an alternative to the asymptotic approximation in small s...

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. The methods that are available for implementing the bootstrap and the accuracy of bootstrap estimates depend on whether the data are a random sample from a distribution or a time series. This paper is concerned with the application of the boots...