Stationarity has always played an important part in forecasting theory. However, some ecomic time series show time-varying autocovariances. The question arises whether forecasts can be improved using models that capture such a time-varying second-order structure. One possibility is given by autoregressive models with time-varying parameters. The author focuses on the development of a forecasting procedure for these processes and compares this approach to classical forecasting methods by means of Monte Carlo simulations. An evaluation of the proposed procedure is given by its application to futures prices and the Dow Jones index. The approach turns out to be superior to the classical methods if the sample sizes are large and the forecasting horizons do t range too far into the future.
Tina Loll holds a Diploma in Civil Engineering from the University of Duisburg-Essen and a Diploma in Business Administration and Engineering from the University of Bochum. From 2007 to 2011 she worked as a research assistant at the Institute of Statistics and Econometrics of the University of Hamburg and received a Doctor of Economics.