This study presents a number of short-term ex-post forecasts of single equation model, Multivariate Autoregressive Moving Average (MARMA) model, simultaneous supply-demand and price system equation model, and Autoregressive Integrated Moving Average (ARIMA) model, and ARCH-type models of natural rubber (NR) SMR20 (Standard Malaysia Rubber of grade 20) prices in the world NR market. The ARCH-type models (Autoregressive Conditional Heteroskedasticity) used include the GARCH (1,1) (Generalized ARCH) model, EGARCH (1,1) (The Exponential GARCH) model, PARCH (1,1) (The Power ARCH) and CGARCH (1,1) (The Component GARCH) model. The models were utilized using monthly data from January 1990 to December 2008 as estimation period, providing a total of 228 observations and data was used as an ex-post forecasts. The results revealed that the forecasting performance of the simultaneous supply-demand and price system equation model was more efficient than single equation model, MARMA model and ARIMA model, and ARCH-type models for ex-post forecast in estimating the price of SMR20 in the next 6 months or so.
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Aye Aye Khin has successfully obtained her Ph.D Degree in Agribusiness and worked as a Post Doctoral Research Fellow at Universiti Putra Malaysia (UPM) in 2010. In 2011, she has joined as a Lecturer in Multimedia University (MMU), Cyberjaya, Malaysia and until to now.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This study presents a number of short-term ex-post forecasts of single equation model, Multivariate Autoregressive Moving Average (MARMA) model, simultaneous supply-demand and price system equation model, and Autoregressive Integrated Moving Average (ARIMA) model, and ARCH-type models of natural rubber (NR) SMR20 (Standard Malaysia Rubber of grade 20) prices in the world NR market. The ARCH-type models (Autoregressive Conditional Heteroskedasticity) used include the GARCH (1,1) (Generalized ARCH) model, EGARCH (1,1) (The Exponential GARCH) model, PARCH (1,1) (The Power ARCH) and CGARCH (1,1) (The Component GARCH) model. The models were utilized using monthly data from January 1990 to December 2008 as estimation period, providing a total of 228 observations and data was used as an ex-post forecasts. The results revealed that the forecasting performance of the simultaneous supply-demand and price system equation model was more efficient than single equation model, MARMA model and ARIMA model, and ARCH-type models for ex-post forecast in estimating the price of SMR20 in the next 6 months or so. 236 pp. Englisch. Bestandsnummer des Verkäufers 9783846515464
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khin Aye AyeAye Aye Khin has successfully obtained her Ph.D Degree in Agribusiness and worked as a Post Doctoral Research Fellow at Universiti Putra Malaysia (UPM) in 2010. In 2011, she has joined as a Lecturer in Multimedia Universi. Bestandsnummer des Verkäufers 5495983
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Taschenbuch. Zustand: Neu. Econometric Forecasting Models for Short Term Natural Rubber Prices | Economic Development of World Natural Rubber Industry Models Specifications, Simulation and Evaluation | Aye Aye Khin (u. a.) | Taschenbuch | 236 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846515464 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 106773073
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This study presents a number of short-term ex-post forecasts of single equation model, Multivariate Autoregressive Moving Average (MARMA) model, simultaneous supply-demand and price system equation model, and Autoregressive Integrated Moving Average (ARIMA) model, and ARCH-type models of natural rubber (NR) SMR20 (Standard Malaysia Rubber of grade 20) prices in the world NR market. The ARCH-type models (Autoregressive Conditional Heteroskedasticity) used include the GARCH (1,1) (Generalized ARCH) model, EGARCH (1,1) (The Exponential GARCH) model, PARCH (1,1) (The Power ARCH) and CGARCH (1,1) (The Component GARCH) model. The models were utilized using monthly data from January 1990 to December 2008 as estimation period, providing a total of 228 observations and data was used as an ex-post forecasts. The results revealed that the forecasting performance of the simultaneous supply-demand and price system equation model was more efficient than single equation model, MARMA model and ARIMA model, and ARCH-type models for ex-post forecast in estimating the price of SMR20 in the next 6 months or so.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 236 pp. Englisch. Bestandsnummer des Verkäufers 9783846515464
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Zustand: New. PRINT ON DEMAND pp. 236. Bestandsnummer des Verkäufers 1898162313
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This study presents a number of short-term ex-post forecasts of single equation model, Multivariate Autoregressive Moving Average (MARMA) model, simultaneous supply-demand and price system equation model, and Autoregressive Integrated Moving Average (ARIMA) model, and ARCH-type models of natural rubber (NR) SMR20 (Standard Malaysia Rubber of grade 20) prices in the world NR market. The ARCH-type models (Autoregressive Conditional Heteroskedasticity) used include the GARCH (1,1) (Generalized ARCH) model, EGARCH (1,1) (The Exponential GARCH) model, PARCH (1,1) (The Power ARCH) and CGARCH (1,1) (The Component GARCH) model. The models were utilized using monthly data from January 1990 to December 2008 as estimation period, providing a total of 228 observations and data was used as an ex-post forecasts. The results revealed that the forecasting performance of the simultaneous supply-demand and price system equation model was more efficient than single equation model, MARMA model and ARIMA model, and ARCH-type models for ex-post forecast in estimating the price of SMR20 in the next 6 months or so. Bestandsnummer des Verkäufers 9783846515464
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