Bias correction in the frequency domain estimation of time series models

Abstract

Exact expressions for the expectations and covariances of frequency domain estimators for integrals of the spectrum of a stationary time series are derived. It is demonstrated that the obvious correction for bias improves the estimator even when observations are missing. The techniques are applied to frequency domain estimation of model parameters, and this is illustrated by an application on a medical time series with missing observations.