Testing mean stability of heteroscedastic time series


Speaker:Luidas Giraitis, Queen Mary, University of London
Date: Wednesday 15 February 2017
Time: 14.30
Location: Bateman Lecture Theatre, Building One

Further details

Time series models are often fi tted to the data without preliminary checks for stability of the mean and variance, conditions that may not hold in much economic and financial data, particularly over long periods. Ignoring such shifts may result in fi tting models with spurious dynamics that lead to unsupported and controversial conclusions about time dependence, causality, and the e ffects of unanticipated shocks. In spite of what may seem as obvious differences between a time series of independent variates with changing variance and a stationary conditionally heteroskedastic (GARCH) process, such processes may be hard to distinguish in applied work using basic time series diagnostic tools. We develop and study some practical and easily implemented statistical procedures to test the mean and variance stability of uncorrelated and serially dependent time series. Application of the new methods to analyze the volatility properties of stock market returns leads to some unexpected surprising findings concerning the advantages of modeling time varying changes in unconditional variance.