Forecasting Economic Time Series using Locally Stationary Processes: A New Approach with Applications (Volkswirtschaftliche Analysen) - Hardcover
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.
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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.
"About this title" may belong to another edition of this title.
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Forecasting economic time series using locally stationary processes : a new approach with applications. Volkswirtschaftliche Analysen ; Bd. 19.
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Frankfurt, M. u.a. : Lang, 2012
ISBN 10: 3631621876
ISBN 13: 9783631621875
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Originalhardcover. Condition: Sehr gut. 138 S. : graph. Darst. Ein tadelloses Exemplar. - 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. ISBN 9783631621875 Sprache: Englisch Gewicht in Gramm: 288. Seller Inventory # 1083716
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Forecasting Economic Time Series using Locally Stationary Processes
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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
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Forecasting Economic Time Series using Locally Stationary Processes
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Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Forecasting Economic Time Series using Locally Stationary ProcessesStationarity 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. Seller Inventory # 117177300
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Forecasting Economic Time Series using Locally Stationary Processes
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Peter Lang, Peter Lang Jan 2012, 2012
ISBN 10: 3631621876
ISBN 13: 9783631621875
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Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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.Lang, Peter GmbH, Gontardstraße 11, 10178 Berlin 140 pp. Englisch. Seller Inventory # 9783631621875
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Forecasting Economic Time Series using Locally Stationary Processes : A New Approach with Applications
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783631621875