Click Here to Download: https://ouo.io/0w7swB Forecasting Economic Time Series using Locally Stationary Processes A New Approach with Applications By: Tina Loll Publisher: Peter Lang GmbH, Internationaler Verlag der Wissenschaften Print ISBN: 9783631621875, 3631621876 eText ISBN: 9783653017069, 3653017068 Edition: 1st Format: PDF Available from $ 40.95 USD SKU 9783653017069 Stationarity has always played an important part in forecasting theory. However, some economic 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 not range too far into the future.
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