EstimatedWold Representation and Spectral Density-Driven Bootstrap for Time Series

TL;DR

This paper introduces a spectral density-driven bootstrap method for stationary time series, leveraging Wold representation coefficients estimated from spectral density, and demonstrates its asymptotic validity and practical performance through simulations and real data analysis.

Contribution

It develops a new bootstrap approach based on spectral density estimation and Wold coefficients, unifying and extending existing bootstrap methods for linear time series.

Findings

The bootstrap asymptotically works for a wide range of statistics.

The method performs well in finite samples according to simulations.

It generalizes the autoregressive-sieve bootstrap when using parametric spectral estimators.

Abstract

The second-order dependence structure of purely nondeterministic stationary process is described by the coefficients of the famous Wold representation. These coefficients can be obtained by factorizing the spectral density of the process. This relation together with some spectral density estimator is used in order to obtain consistent estimators of these coefficients. A spectral density-driven bootstrap for time series is then developed which uses the entire sequence of estimated MA coefficients together with appropriately generated pseudo innovations in order to obtain a bootstrap pseudo time series. It is shown that if the underlying process is linear and if the pseudo innovations are generated by means of an i.i.d. wild bootstrap which mimics, to the necessary extent, the moment structure of the true innovations, this bootstrap proposal asymptotically works for a wide range of statistics. The relations of the proposed bootstrap procedure to some other bootstrap procedures, including the autoregressive-sieve bootstrap, are discussed. It is shown that the latter is a special case of the spectral density-driven bootstrap, if a parametric autoregressive spectral density estimator is used. Simulations investigate the performance of the new bootstrap procedure in finite sample situations. Furthermore, a real-life data example is presented.