Higher-Order estimation of s-th order spectra with flat-top lag-windows

Abstract

Improved performance in higher-order spectral density estimation is achieved using a general class of infinite-order kernels. These estimates are asymptotically less biased but with the same order of variance as compared to the classical estimators with second-order kernels. A simple, data-dependent algorithm for selecting bandwidth is introduced and is shown to be consistent with estimating the optimal bandwidth. Bispectral simulations with several standard models are used to demonstrate the performance of the proposed methodology.