Idioma: Inglés
Publicado por Frankfurt, M. u.a. : Lang, 2012
ISBN 10: 3631621876 ISBN 13: 9783631621875
Librería: Borkert, Schwarz und Zerfaß GbR, Berlin, Alemania
EUR 12,00
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Añadir al carritoOriginalhardcover. Condición: 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.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoBuch. Condición: 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.
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoBuch. Condición: 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.
Librería: moluna, Greven, Alemania
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Añadir al carritoGebunden. Condición: 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.
Idioma: Inglés
Publicado por Peter Lang, Peter Lang Jan 2012, 2012
ISBN 10: 3631621876 ISBN 13: 9783631621875
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 43,60
Cantidad disponible: 1 disponibles
Añadir al carritoBuch. Condición: 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.
Librería: preigu, Osnabrück, Alemania
EUR 43,60
Cantidad disponible: 5 disponibles
Añadir al carritoBuch. Condición: Neu. Forecasting Economic Time Series using Locally Stationary Processes | A New Approach with Applications | Tina Loll | Buch | Englisch | 2012 | Peter Lang | EAN 9783631621875 | Verantwortliche Person für die EU: Lang, Peter GmbH, Gontardstr. 11, 10178 Berlin, r[dot]boehm-korff[at]peterlang[dot]com | Anbieter: preigu Print on Demand.