Forecasting Economic Time Series using Locally Stationary Processes

Tina Loll

ISBN 10: 3631621876 / ISBN 13: 9783631621875

Published by Peter Lang, Peter Lang Jan 2012, 2012

About this Item

Neuware -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. 140 pp. Englisch.

Seller Inventory # 9783631621875

Bibliographic Details

• Title: Forecasting Economic Time Series using Locally Stationary Processes
• Publisher: Peter Lang, Peter Lang Jan 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3631621876
• ISBN 13: 9783631621875
• Binding: Buch
• Condition: Neu
• Dimensions: 216W x 153H x 11D millimeters 216 millimeters width by 153 millimeters height by 11 millimeters depth
• Weight: 303 grams

About this title

Synopsis

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.

About the Author

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.