We develop theory of a novel fast bootstrap for dependent data. Our scheme deploys i.i.d. resampling of

smoothed moment indicators. We characterize the class of parametric and semiparametric estimation

problems for which the method is valid. We show the asymptotic refinements of the new procedure,

proving that it is higher-order correct under mild assumptions on the time series, the estimating func-

tions, and the smoothing kernel. We illustrate the applicability and the advantages of our procedure

for M-estimation, generalized method of moments, and generalized empirical likelihood estimation. In

a Monte Carlo study, we consider an autoregressive conditional duration model and we compare our

method with other extant, routinely-applied first- and higher-order correct methods. The results pro-

vide numerical evidence that the novel bootstrap yields higher-order accurate confidence intervals, while

remaining computationally lighter than its higher-order correct competitors. A real-data example on

dynamics of trading volume of US stocks illustrates the empirical relevance of our method.