Title: Estimating the frequency of a time series
Authors: Liudas Giraitis - Queen Mary University of London (United Kingdom)
Karim Abadir - Imperial College London (United Kingdom)
Walter Distaso - Imperial College London (United Kingdom) [presenting]
Abstract: A novel methodology is presented that allows us to estimate both the long memory parameter of a (possibly nonstationary) time series and the frequency which maximizes the spectral density. We derive limiting distributions, assess them through a simulation exercise and also provide a valid bootstrap scheme that uses both time domain and frequency domain information. We then illustrate the usefulness of our model in capturing the salient features of dynamics of different datasets.