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http://dx.doi.org/10.7744/kjoas.20180056

Comparison of forecasting performance of time series models for the wholesale price of dried red peppers: focused on ARX and EGARCH  

Lee, Hyungyoug (Korea Rural Economic Institute)
Hong, Seungjee (Department of Agricultural Economics, Chungnam National University)
Yeo, Minsu (Hanwha Hotels&Resorts Food Culture)
Publication Information
Korean Journal of Agricultural Science / v.45, no.4, 2018 , pp. 859-870 More about this Journal
Abstract
Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.
Keywords
ARX (AR model with an exogenous variables); dried red pepper; forecasting performance; time series analysis EGARCH;
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1 Cho JH, Lee HS. 2011. Development of a mid-term preceding observation model for radish. CNU Journal of Agricultural Science 38:571-581. [in Korean]
2 Han SH, Lee JM, Park MS, Park YK, Jang SJ. 2010. KREI Monthly Outlook Model. M102. Korea Rural Economic Institute, Naju, Korea. [in Korean]
3 Hwang ES, Ahn BI. 2012. Analyses on the characteristics of the trend and volatility of the major fruits and vegetables. The Korean Journal of Agricultural Economics 53:1-21. [in Korean]
4 Lee HY, Yeo MS, Hong SJ. 2017. Comparison of time series forecasting models in garlic's wholesale price. Journal of Rural Development 40:55-73. [in Korean]
5 Kim BS. 2005. A comparison of forecasting performance of the application models for forecasting of vegetable prices. The Korean Journal of Agricultural Economics 46:89-113. [in Korean]
6 Kim BS, Park MS, Cho JH, Kim TK. 2010. A demand and supply model of agricultural and livestock products for midterm outlook. M103. Korea Rural Economic Institute, Naju, Korea. [in Korean]
7 Kim IS, Chung GY, Byeon AR. 2014. Monthly structural models of Korean onion and green onion markets. N2014-34. Korea Rural Economic Institute, Naju, Korea. [in Korean]
8 Kim IS, Kim L, Byun SY, Kim HY. 2013. Monthly structural models of Korean dried pepper and garlic markets. N2013-34. Korea Rural Economic Institute, Naju, Korea. [in Korean]
9 Kim SD, Yang SR. 2016. A study on the marketing channels onto Garak agricultural market -Auction system vs. unlisted transaction-. The Korean Journal of Agricultural Economics 33:19-45. [in Korean]
10 MAFRA (Ministry of Agriculture, Food and Rural Affairs). 2018. 2018 major statistics on agriculture, food and rural affairs. MAFRA, Sejong, Korea. [in Korean]
11 Nelson DB. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59:347-370.   DOI
12 Park JY, Park YG. 2013. The development of Chinese cabbage and radish forecast models. M125. Korea Rural Economic Institute, Naju, Korea. [in Korean]
13 Yoo DI. 2016. Developing vegetable price forecasting model with climate factors. The Korean Journal of Agricultural Economics 57:1-24. [in Korean]
14 aT KAMIS. KAMIS. Accessed in http://www.kamis.or.kr on 10 March 2018.