The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

The chapter gives a review of the literature on bootstrap methods for time series data. It describes various possibilities on how the bootstrap method, initially introduced for independent random variables, can be extended to a wide range of dependent variables in discrete time, including parametric or nonparametric time series models, autoregressive and Markov processes, long range dependent t...

The parametric bootstrap tests and the asymptotic or approximate tests for detecting difference of two Poisson means are compared. The test statistics used are the Wald statistics with and without log-transformation, the Cox F statistic and the likelihood ratio statistic. It is found that the type I error rate of an asymptotic/approximate test may deviate too much from the nominal significance ...

Non-Homogeneous Gamma Process (NHGP) is characterized by an arbitrary trend function and a gamma renewal distribution. In this paper, we estimate the confidence intervals of model parameters of NHGP via two parametric bootstrap methods: simulation-based approach and re-sampling-based approach. For each bootstrap method, we apply three methods to construct the confidence intervals. Through simul...

Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bootstrap smoothing, also known as bagging, to tame the erratic discontinuities of selection-based estimators. A projection theorem then provides standard ...

many empirical papers have applied a semi-parametric two-stage procedure named tobit model to investigate the sources of inefficiency in different industries over the last two decades. using this approach in small samples has recently been criticized for a possible bias in its results. in very recent papers simar and wilson (2007) have tackled this problem by suggesting an alternative bootstrap...

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...

This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein variance estimator.

This note concerns the use of parametric bootstrap sampling to carry out Bayesian inference calculations. This is only possible in a subset of those problems amenable to Markov-Chain Monte Carlo (MCMC) analysis, but when feasible the bootstrap approach offers both computational and theoretical advantages. The discussion here is in terms of a simple example, with no attempt at a general analysis.

Bootstrap tests are tests for which the signiicance level is calculated by some sort of bootstrap procedure, which may be parametric or nonparametric. We show that, in many circumstances, the size distortion of a bootstrap P value for a test will be one whole order of magnitude smaller than that of the corresponding asymptotic P value. We also show that, at least in the parametric case, the mag...