A0814
Title: Estimation of random cycles in persistent time series
Authors: Karim Abadir - Imperial College London (United Kingdom)
Natalia Bailey - Monash University (Australia)
Walter Distaso - Imperial College London (United Kingdom)
Liudas Giraitis - Queen Mary University of London (United Kingdom) [presenting]
Abstract: A number of economic, financial, and climatic time series exhibit persistent cycles which are characterized by dependence patterns and peaks in the spectrum. A class of semiparametric cyclical-memory processes is introduced, which enable the modelling of random cyclical patterns in stationary and non-stationary time series. A theoretical background and asymptotic estimation theory are developed for the frequency of a cycle represented by the location of a peak in the spectrum. The estimation procedure is easy to implement and allows for constructing narrow confidence intervals around the location point. Monte Carlo simulations confirm the estimator's good finite sample performance. The method is illustrated with three empirical applications. Quasi- periodic cycles are uncovered in macroeconomic series, both nominal and real (US nominal GDP and real industrial production), and CO2 concentration levels.
