A Forecast of Shipping Business during the Year of 2013

해운경기의 예측: 2013년

  • Received : 2013.02.12
  • Accepted : 2013.03.29
  • Published : 2013.03.31

Abstract

It has been more than four years since the outbreak of global financial crisis. However, the world economy continues to be challenged with new crisis such as the European debt crisis and the fiscal cliff issue of the U.S. The global economic environment remains fragile and prone to further disappointment, although the balance of risks is now less skewed to the downside than it has been in recent years. It's no wonder that maritime business will be bearish since the global business affects the maritime business directly as well as indirectly. This paper, hence, aims to predict the Baltic Dry Index representing the shipping business using the ARIMA-type models and Hodrick-Prescott filtering technique. The monthly data cover the period January 2000 through January 2013. The out-of-sample forecasting performance is measured by three summary statistics: root mean squared percent error, mean absolute percent error and mean percent error. These forecasting performances are also compared with those of the random walk model. This study shows that the ARIMA models including Intervention-ARIMA have lower rmse than random walk model. This means that it's appropriate to forecast BDI using the ARIMA models. This paper predicts that the shipping market will be more bearish in 2013 than the year 2012. These pessimistic ex-ante forecasts are supported by the Hodrick-Prescott filtering technique.

해운경기와 밀접한 관계를 갖는 세계 경기가 유럽재정위기와 같은 일련의 사건으로 침체국면에서 벗어나지 못하고 있어 장기적인 해운시황에 대한 우려가 커지고 있으며, BDI 건화물선 종합운임지수가 1000포인트에도 도달하지 못해 해운기업의 어려움을 가중시키고 있다. 본고는 해운경기의 불황탈피가 2013년에 가능한가를 파악하기 위해 BDI를 예측하는데 목적을 둔다. 해상운임에 영향을 미치는 변수들로 구성된 다변량모형 대신 BDI로만 구성된 단일변량모형인 자기회귀-이동평균모형과 장기순환과정을 보여주는 Hodrick-Prescott 필터 기법을 이용하여 2013년의 BDI를 예측한다. 3개의 ARIMA모형과 2개의 개입-ARIMA 모형을 이용하여 2013년에도 지속적으로 BDI가 하락하는 760과 670사이에서 움직인다는 것을 보인다. HP기법을 통한 예측은 750에서 556사이의 변동을 예상하여 ARIMA모형보다 해운경기를 더 비관적이라는 것도 밝힌다. 또한 5개의 ARIMA모형의 예측오류가 RW모형보다 낮을 뿐만 아니라 그 크기가 대단히 작아 예측치가 크게 빗나갈 가능성이 낮다는 것도 보인다.

Keywords

References

  1. 김종원.한동근, "개입-ARIMA모형을 이용한 서울시의 물 수요예측", 국토연구, 제32권, 2001, pp.51-61.
  2. 김태구.송두석, "ARIMA모형을 적용한 외국인 이용객 호텔객실 수요예측모형 선정: 서울 특1급 호텔을 중심으로", 호텔경영학연구, 제15권 제5호, 2006, pp. 97-118.
  3. 모수원, "ARIMA모형을 이용한 2011년 BDI의 예측", 한국항만경제학회지, 제26집 제4호, 2010, pp. 207-218.
  4. LG경제연구원, "2013년 경제전망", LG Business Insight, 2012.9.
  5. Akal, M., "Forecasting Turkey's Tourism Revenues by ARMAX Model," Tourism Management, 25, 2005, 565-580.
  6. Chu, F.L., "Forecasting Tourism Arrivals: Nonlinear Sine Wave or ARIMA", Journal of Travel Research, 36, 1998, 79-84. https://doi.org/10.1177/004728759803600309
  7. Goh, C. and Law, R., "Modeling and Forecasting Tourism Demand for Arrivals with Stochastic Nonstationary Seasonality and Intervention," Tourism Management, 23, 2002, 499-510. https://doi.org/10.1016/S0261-5177(02)00009-2
  8. Gujarati, D.N., Basic Econometrics, McGraw-Hill, Inc., 1995, p.735.
  9. Hodrick, R.J. and Prescott, E.C., "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit, and Banking, 29, 1997, 1-16. https://doi.org/10.2307/2953682
  10. MacDonald, R. and Taylor, M.P.(1993), "The Monetary Approach to the Exchange Rate," IMF Staff Papers, 40(1), pp. 89-107. https://doi.org/10.2307/3867378
  11. Meese, R.A. and Rogoff, K., "Empirical Exchange Rate Models of the Seventies: Do They Fit Out Of Sample?", Journal of International Economics, 14, 1983, 3-24. https://doi.org/10.1016/0022-1996(83)90017-X
  12. Somanath, V.S., "Efficient Exchange Rate Forecasts: Lagged Models Better than the Random Walk," Journal of International Money and Finance, 5, 1986, 195-220. https://doi.org/10.1016/0261-5606(86)90042-2
  13. Turner, L. and Witt, S.F., "Forecasting Tourism using Univariate and Multivariate Structural Time Series Models," Tourism Economics, 7(2), 2001, 135-147. https://doi.org/10.5367/000000001101297775