시계열 분석을 이용한 굴 가격 예측에 관한 연구
A Study on Forecasting Oyster Price with Time Series Analysis
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초록
This paper forecasts one-month ahead price of the real landing price of the Korean oyster farmed with high price fluctuations by month. To forecast the one-month ahead real landing price of the farmed oyster, this paper uses monthly data (128 observations) from January 2001 to August 2011 and also adopts several econometrics methods such as the multiple regression model, the autoregressive integrated moving average model, and the vector autoregression model. As a result, the one-month ahead real landing price of the oyster forecasted by the multiple regression model had relatively lower prediction error than ones of ARIMA(2,0,0) and VAR models. Particularly, first, the one-month ahead real landing price of the oyster forecasted by the multiple regression model was 4,907 won per kg with prediction error of about 1.21 won. Second, the one-month ahead price of the ARIMA(2,0,0) model was forecasted as 4,652.13 won per kg with prediction error of approximately 257won. Third, the one-month ahead price of oyster based on the VAR model was estimated as 4,386.43 won per kg with prediction error of 522.57 won. However, basing on root mean squared error, mean absolute error, mean absolute percentage error, and Theil inequality coefficient, the one-month ahead price of oyster by the VAR model was fitter than one by the ARIMA(2,0,0) and multiple regression models. In conclusion, this paper suggests that out-of-sample forecasts as 12months ahead need in order to find the best model among the three models.
Abstract
This paper forecasts one-month ahead price of the real landing price of the Korean oyster farmed with high price fluctuations by month. To forecast the one-month ahead real landing price of the farmed oyster, this paper uses monthly data (128 observations) from January 2001 to August 2011 and also adopts several econometrics methods such as the multiple regression model, the autoregressive integrated moving average model, and the vector autoregression model. As a result, the one-month ahead real landing price of the oyster forecasted by the multiple regression model had relatively lower prediction error than ones of ARIMA(2,0,0) and VAR models. Particularly, first, the one-month ahead real landing price of the oyster forecasted by the multiple regression model was 4,907 won per kg with prediction error of about 1.21 won. Second, the one-month ahead price of the ARIMA(2,0,0) model was forecasted as 4,652.13 won per kg with prediction error of approximately 257won. Third, the one-month ahead price of oyster based on the VAR model was estimated as 4,386.43 won per kg with prediction error of 522.57 won. However, basing on root mean squared error, mean absolute error, mean absolute percentage error, and Theil inequality coefficient, the one-month ahead price of oyster by the VAR model was fitter than one by the ARIMA(2,0,0) and multiple regression models. In conclusion, this paper suggests that out-of-sample forecasts as 12months ahead need in order to find the best model among the three models.
참고문헌 (26)
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