We develop theory of a novel fast bootstrap for dependent data. Our scheme deploys 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 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-applie firs - and higher-order correct methods. The results provide 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.

Keywords: Fast bootstrap methods, Higher-order refinements, Generalized Empirical Likelihood, Confidence distributions, Mixing processes.

JEL: C12, C15, C22, C52, C58, G12.