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Forecasting Economic Time Series using Locally Stationary Processes: A New Approach with Applications - Couverture rigide
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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À propos de l?auteur
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.
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Forecasting economic time series using locally stationary processes : a new approach with applications. Volkswirtschaftliche Analysen ; Bd. 19.
Edité par
Frankfurt, M. u.a. : Lang, 2012
ISBN 10 : 3631621876
ISBN 13 : 9783631621875
Ancien ou d'occasion
Couverture rigide
Vendeur : Fundus-Online GbR Borkert Schwarz Zerfaß, Berlin, Allemagne
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Originalhardcover. Etat : 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. N° de réf. du vendeur 1083716
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Forecasting Economic Time Series using Locally Stationary Processes
Edité par
Peter Lang Jan 2012, 2012
ISBN 10 : 3631621876
ISBN 13 : 9783631621875
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9783631621875
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Forecasting Economic Time Series using Locally Stationary Processes
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Gebunden. Etat : 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. N° de réf. du vendeur 117177300
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Forecasting Economic Time Series using Locally Stationary Processes
Edité par
Peter Lang, Peter Lang Jan 2012, 2012
ISBN 10 : 3631621876
ISBN 13 : 9783631621875
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : Neu. 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. N° de réf. du vendeur 9783631621875
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Forecasting Economic Time Series using Locally Stationary Processes : A New Approach with Applications
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9783631621875
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Forecasting Economic Time Series using Locally Stationary Processes | A New Approach with Applications
impression à la demandeVendeur : preigu, Osnabrück, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 106625593
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Forecasting Economic Time Series using Locally Stationary Processes: A New Approach with Applications (Volkswirtschaftliche Analysen)
Edité par
Peter Lang GmbH, Internationaler Verlag der Wissen, 2012
ISBN 10 : 3631621876
ISBN 13 : 9783631621875
Ancien ou d'occasion
Couverture rigide
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Hardcover. Etat : Very Good. Very Good. book. N° de réf. du vendeur ERICA82936316218766
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