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Deseasonalized State-Space Time Series Forecasting with Application to the US Salmon Market
GUANG GU and JAMES L. ANDERSON
Marine Resource Economics
Vol. 10, No. 2 (Summer 1995), pp. 171-185
Published by: The University of Chicago Press
Stable URL: http://www.jstor.org/stable/42629109
Page Count: 15
You can always find the topics here!Topics: Forecasting models, Analytical forecasting, Modeling, Parametric models, Time series models, Time series forecasting, Salmon market, Salmon, Raw data, Time series
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An approach that combines seasonality removal with a multivariate, state-space, time series forecasting model is developed to provide shortrun forecasts for the US salmon market. Time series included in the model are: US fresh Atlantic salmon wholesale price index; fresh salmon (Atlantic, coho and chinook) monthly US import quantities and prices; and US chum and sockeye salmon monthly export prices. Four versions of the state-space forecasting model are compared in terms of their statistical performance during out-of-s ample forecasts. Out-of-sample 3-, 6- and 12-month ahead directional predictions are generated to test the models' performance in terms of direction. Under identical modeling conditions, out-of-sample statistical and directional tests indicate that deseasonalization improves the overall performance of the state-space model. As a result, a linear, deseasonalized, state-space forecasting model is selected to provide twelve monthly out-of-sample forecasts for all series.
Marine Resource Economics © 1995 MRE Foundation, Inc.