  We develop and implement a novel fast bootstrap for dependent data. Our
scheme is based on the i.i.d. resampling of the smoothed moment indicators. We
characterize the class of parametric and semi-parametric estimation problems
for which the method is valid. We show the asymptotic refinements of the
proposed procedure, proving that it is higher-order correct under mild
assumptions on the time series, the estimating functions, and the smoothing
kernel. We illustrate the applicability and the advantages of our procedure for
Generalized Empirical Likelihood estimation. As a by-product, our fast
bootstrap provides higher-order correct asymptotic confidence distributions.
Monte Carlo simulations on an autoregressive conditional duration model provide
numerical evidence that the novel bootstrap yields higher-order accurate
confidence intervals. A real-data application on dynamics of trading volume of
stocks illustrates the advantage of our method over the routinely-applied
first-order asymptotic theory, when the underlying distribution of the test
statistic is skewed or fat-tailed.
