A higher-order correct fast moving-average bootstrap for dependent data - new publication by Olivier Scaillet
In a new study, GFRI's Director Olivier Scaillet and his co-authors develop the theory of a novel fast bootstrap for dependent data.
Their scheme deploys i.i.d. resampling of smoothed moment indicators. They characterize the class of parametric and semiparametric estimation problems for which the method is valid. They show the asymptotic refinements of the new procedure, proving that it is higher-order correct under mild assumptions on the time series, the estimating functions, and the smoothing kernel.
They illustrate the applicability and the advantages of their procedure for M-estimation, generalized method of moments, and generalized empirical likelihood estimation. In a Monte Carlo study, they consider an autoregressive conditional duration model and they compare their method with other extant, routinely-applied first- 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 their method.
The paper was co-authored with Davide La Vecchia and Alban Moor, and is forthcoming in the Journal of Econometrics.
May 15, 2023