The Journal of Fisheries Business Administration (수산경영론집)

The Korean Society of Fisheries Business Administration (한국수산경영학회)
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A Comparison of Predictive Power among Forecasting Models of Monthly Frozen Mackerel Consumer Price Models

냉동 고등어 소비자가격 모형 간 예측력 비교

  • Jeong, Min-Gyeong (Department of Resource and Environmental Economics, Graduate School, Pukyong National University) ;
  • Nam, Jong-Oh (Division of Economics, College of Humanities & Social Sciences, Pukyong National University)
  • 정민경 (부경대학교 일반대학원 자원환경경제학과) ;
  • 남종오 (부경대학교 인문사회과학대학 경제학부)
  • Received : 2021.11.30
  • Accepted : 2021.12.21
  • Published : 2021.12.31
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Abstract

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

Keywords

Acknowledgement

이 논문은 2017년 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2017S1A6A3A01079869).

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