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Forecasting the Baltic Dry Index Using Bayesian Variable Selection

베이지안 변수선택 기법을 이용한 발틱건화물운임지수(BDI) 예측

  • Xiang-Yu Han (Department of International Trade, Jeonbuk National University) ;
  • Young Min Kim (Department of International Trade, Jeonbuk National University)
  • Received : 2022.09.07
  • Accepted : 2022.10.30
  • Published : 2022.10.30
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Abstract

Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.

Keywords

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