시계열 모형을 이용한 김 위판가격 예측에 관한 연구
Forecast for Laver Producer Price Using Time Series Models
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초록
This paper forecasts one-month and twelve-month ahead prices of the real producer price of the Korean laver farmed with high price fluctuations by month. To forecast the one-month and twelve-month ahead real producer prices of the farmed laver, this paper uses monthly data (98 observations) from October 2004 to November 2012 and also adopts several econometrics methods such as the multiple regression model, the autoregressive integrated moving average model, and the vector error correction model. As a result, the one-month and twelve-month ahead real producer prices of the laver forecasted by the multiple regression model had relatively lower prediction errors than ones of several ARIMA models and a VECM model. In particular, first, the one-month and twelve-month ahead real producer prices of the laver forecasted by the multiple regression model was 945.11 won and 1,003.32 won per kg with prediction errors of 58.11 won and –7.68 won. Secondly, the one-month and twelve-month ahead prices of the ARIMA(1,0,0) model was forecasted as 1,017.40 won and 721.33 won per kg with prediction errors of 130.40 won and –289.67 won. Thirdly, the one-month and twelve-month ahead prices of the laver based on the VECM model was estimated as 1,194.45 won and 623.04 won per kg with prediction errors of 307.45 won and –387.96, respectively. However, in comparison among a root mean squared error, a mean absolute error, a mean absolute percentage error, and a Theil inequality coefficient, the one-month and twelve-month ahead prices of farmed laver by the multiple regression model was fitter than ones by the ARIMA(1,0,0) and the VECM. In conclusion, this paper suggests that out-of-sample forecasts as 36 months ahead using Diebold & Mariano test need in order to find the best model among the three models.
Abstract
This paper forecasts one-month and twelve-month ahead prices of the real producer price of the Korean laver farmed with high price fluctuations by month. To forecast the one-month and twelve-month ahead real producer prices of the farmed laver, this paper uses monthly data (98 observations) from October 2004 to November 2012 and also adopts several econometrics methods such as the multiple regression model, the autoregressive integrated moving average model, and the vector error correction model. As a result, the one-month and twelve-month ahead real producer prices of the laver forecasted by the multiple regression model had relatively lower prediction errors than ones of several ARIMA models and a VECM model. In particular, first, the one-month and twelve-month ahead real producer prices of the laver forecasted by the multiple regression model was 945.11 won and 1,003.32 won per kg with prediction errors of 58.11 won and –7.68 won. Secondly, the one-month and twelve-month ahead prices of the ARIMA(1,0,0) model was forecasted as 1,017.40 won and 721.33 won per kg with prediction errors of 130.40 won and –289.67 won. Thirdly, the one-month and twelve-month ahead prices of the laver based on the VECM model was estimated as 1,194.45 won and 623.04 won per kg with prediction errors of 307.45 won and –387.96, respectively. However, in comparison among a root mean squared error, a mean absolute error, a mean absolute percentage error, and a Theil inequality coefficient, the one-month and twelve-month ahead prices of farmed laver by the multiple regression model was fitter than ones by the ARIMA(1,0,0) and the VECM. In conclusion, this paper suggests that out-of-sample forecasts as 36 months ahead using Diebold & Mariano test need in order to find the best model among the three models.
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