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A Study on Forecast of Oyster Production using Time Series Models
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  • Journal title : Ocean and Polar Research
  • Volume 34, Issue 2,  2012, pp.185-195
  • Publisher : Korea Institute of Ocean Science & Technology
  • DOI : 10.4217/OPR.2012.34.2.185
 Title & Authors
A Study on Forecast of Oyster Production using Time Series Models
Nam, Jong-Oh; Noh, Seung-Guk;
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This paper focused on forecasting a short-term production of oysters, which have been farmed in Korea, with distinct periodicity of production by year, and different production level by month. To forecast a short-term oyster production, this paper uses monthly data (260 observations) from January 1990 to August 2011, and also adopts several econometrics methods, such as Multiple Regression Analysis Model (MRAM), Seasonal Autoregressive Integrated Moving Average (SARIMA) Model, and Vector Error Correction Model (VECM). As a result, first, the amount of short-term oyster production forecasted by the multiple regression analysis model was 1,337 ton with prediction error of 246 ton. Secondly, the amount of oyster production of the SARIMA I and II models was forecasted as 12,423 ton and 12,442 ton with prediction error of 11,404 ton and 11,423 ton, respectively. Thirdly, the amount of oyster production based on the VECM was estimated as 10,425 ton with prediction errors of 9,406 ton. In conclusion, based on Theil inequality coefficient criterion, short-term prediction of oyster by the VECM exhibited a better fit than ones by the SARIMA I and II models and Multiple Regression Analysis Model.
oyster;forecast;multiple regression analysis model;seasonal autoregressive integrated moving average model;vector error correction model;
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