Journal of Korean Society of Rural Planning (농촌계획)

Korean Society of Rural Planning (한국농촌계획학회)
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Improving Forecasting Performance for Onion and Garlic Prices

양파와 마늘가격 예측모형의 예측력 고도화 방안

  • Ha, Ji-Hee (Agricultural Economics, Chungbuk National University) ;
  • Seo, Sang-Taek (Agricultural Economics, Chungbuk National University) ;
  • Kim, Seon-Woong (Agricultural Economics, Chungbuk National University)
  • 하지희 (충북대학교 농업경제학과) ;
  • 서상택 (충북대학교 농업경제학과) ;
  • 김선웅 (충북대학교 농업경제학과)
  • Received : 2019.08.01
  • Accepted : 2019.11.27
  • Published : 2019.11.30
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Abstract

The purpose of this study is to present a time series model of onion and garlic prices. After considering the various time series models, we calculated the appropriate time series models for each item and then selected the model with the minimized error rate by reflecting the monthly dummy variables and import data. Also, we examined whether the predictive power improves when we combine the predictions of the Korea Rural Economic Institute with the predictions of time series models. As a result, onion prices were identified as ARMGARCH and garlic prices as ARXM. Monthly dummy variables were statistically significant for onion in May and garlic in June. Garlic imports were statistically significant as a result of adding imports as exogenous variables. This study is expected to help improve the forecasting model by suggesting a method to minimize the price forecasting error rate in the case of the unstable supply and demand of onion and garlic.

Keywords

References

  1. Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics, 31(3). 307-327. https://doi.org/10.1016/0304-4076(86)90063-1
  2. Engle, R. F., 1982, Autoregressive Conditional Heteroscedasticity with Estimates of Variance of United Kingdom Inflation, Econometrica, 50(4), 987-1008. https://doi.org/10.2307/1912773
  3. Harvey, D. I., S. J. Leybourne, and P. Newbold, 1998, Tests for Forecast Encompassing, Journal of Business and Economic Statistics, 16(2), 254-259. https://doi.org/10.2307/1392581
  4. Kim, B.S., 2005, A Comparison on Forecasting Performance of the Application Models for Forecasting of Vegetable Prices, Korean Journal of Agricultural Economics, 46(4), 89-13.
  5. Kim, S. W., Brorsen, B. W., 2017. Forecasting urea prices. Applied Economics, 49(49), 4970-4981. https://doi.org/10.1080/00036846.2017.1296554
  6. Korea Rural Economic Institute, 2014-2018, Monthly Report(Seasoning vegetables).
  7. Lee, H.Y., Yeo, M.S., Hong, S.J., 2017, Comparison of Garlic Wholesale Price Forecast Series, Journal of Rural Development, 40(2), 55-73. https://doi.org/10.36464/JRD.2017.40.2.003
  8. Lee, S.I., Kim, S.W., Yoon, B.S., 2015, Evaluation of the Performance of Broiler Price Forecasts, Korean Agricultural Policy Review, 42(2), 232-248.
  9. Nam, K.H., Choe, Y.C., 2015 A Study on Onion Wholesale Price Forecasting Model, Korean Journal of Agricultural Extension, 22(4), 423-434.
  10. Rural Development Administration, 2016, Garlic management.
  11. Sanders, D. R. and M. R. Manfredo,, 2004, Comparing Hedging Effectiveness; An Application of the Encompassing Principle, Journal of Agricultural and Resource Economics, 29(1), 31-44.
  12. Shin, J.H., The Future of Agriculture and Farming with the People(2)Fruits and Vegetables, 2018, Agre-Livestock Marketing News.
  13. Lee, H.Y., Han, E.S., Song, S.H., Kim, S.W., 2016, Effect Analysis of Garlic and Onion Farm Purchase Prices, Korean Food Marketing Association Conference.