Published Online:https://doi.org/10.1287/trsc.2021.1046

References

  • Abouarghoub W, Nomikos NK, Petropoulos F (2018) On reconciling macro and micro energy transport forecasts for strategic decision making in the tanker industry. Transportation Res. E: Logist. Transportation Rev. 113:225–238.CrossrefGoogle Scholar
  • Adland R, Cullinane K (2006) The non-linear dynamics of spot freight rates in tanker markets. Transportation Res. E: Logist. Transportation Rev. 42(3):211–224.CrossrefGoogle Scholar
  • Aslani A, Helo P, Naaranoja M (2014) Role of renewable energy policies in energy dependency in Finland: System dynamics approach. Appl. Energy 113:758–765.CrossrefGoogle Scholar
  • Barber P, López-Valcárcel BG (2010) Forecasting the need for medical specialists in Spain: Application of a system dynamics model. Human Resources Health 8(1):24.CrossrefGoogle Scholar
  • Batchelor R, Alizadeh A, Visvikis I (2007) Forecasting spot and forward prices in the international freight market. Internat. J. Forecasting 23(1):101–114.CrossrefGoogle Scholar
  • Bass FM (1969) A new product growth for model consumer durables. Management Sci. 15(5):215–227.LinkGoogle Scholar
  • Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Aeries Analysis: Forecasting and Control, 4th ed. (John Wiley & Sons, New York).Google Scholar
  • Bulut E, Duru O, Yoshida S (2012) A fuzzy integrated logical forecasting (FILF) model of time charter rates in dry bulk shipping: A vector autoregressive design of fuzzy time series with fuzzy c-means clustering. Maritime Econom. Logist. 14(3):300–318.CrossrefGoogle Scholar
  • Chen S, Chen JN (2010) Forecasting container throughputs at ports using genetic programming. Expert Systems Appl. 37(3):2054–2058.CrossrefGoogle Scholar
  • Chen S, Meersman H, Van De Voorde E (2012) Forecasting spot rates at main routes in the dry bulk market. Maritime Econom. Logist. 14(4):498–537.CrossrefGoogle Scholar
  • Cullinane K (1992) A short-term adaptive forecasting model for BIFFEX speculation: A Box–Jenkins approach. Maritime Policy Management 19(2):91–114.CrossrefGoogle Scholar
  • Cullinane K, Mason K, Cape M (1999) A comparison of models for forecasting the Baltic freight index: Box–Jenkins revisited. Internat. J. Maritime Econom. 1(2):15–39.CrossrefGoogle Scholar
  • Fusillo M (2003) Excess capacity and entry deterrence: The case of ocean liner shipping markets. Maritime Econom. Logist. 5(2):100–115.CrossrefGoogle Scholar
  • Dikos G, Marcus HS, Papadatos MP, Papakonstantinou V (2006) Niver lines: A system-dynamics approach to tanker freight modeling. Interfaces 36(4):326–341.LinkGoogle Scholar
  • Duru O (2010) A fuzzy integrated logical forecasting model for dry bulk shipping index forecasting: An improved fuzzy time series approach. Expert Systems Appl. 37(7):5372–5380.CrossrefGoogle Scholar
  • Duru O (2012) A multivariate model of fuzzy integrated logical forecasting method (M-FILF) and multiplicative time series clustering: A model of time-varying volatility for dry cargo freight market. Expert Systems Appl. 39(4):4135–4142.CrossrefGoogle Scholar
  • Duru O (2018) Shipping Business Unwrapped: Illusion, Bias and Fallacy in the Shipping Business (Routledge, London).CrossrefGoogle Scholar
  • Duru O, Bulut E, Yoshida S (2012) A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case. Expert Systems Appl. 39(1):840–848.CrossrefGoogle Scholar
  • Dyson B, Chang NB (2005) Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling. Waste Management 25(7):669–679.CrossrefGoogle Scholar
  • Engle RF, Granger CW (1987) Co-integration and error correction: Representation, estimation, and testing. Econometrica 55(2):251–276.CrossrefGoogle Scholar
  • Engelen S, Dullaert W, Vernimmen B (2009) Market efficiency within dry bulk markets in the short run: A multi-agent system dynamics Nash equilibrium. Maritime Policy Management 36(5):385–396.CrossrefGoogle Scholar
  • Engelen S, Meersman H, Voorde EVD (2006) Using system dynamics in maritime economics: An endogenous decision model for shipowners in the dry bulk sector. Maritime Policy Management 33(2):141–158.CrossrefGoogle Scholar
  • Forrester J (1957) Dynamic models of economic systems and industrial organizations. System Dynamics Group Memo D-0, MIT Press, Cambridge, MA.Google Scholar
  • Forrester JW (1961a) Standard symbols for industrial dynamics flow diagrams. Industrial Dynamics Research Memorandum D-41, MIT Press, Cambridge, MA.Google Scholar
  • Forrester JW (1961b) Stock and Flow Concepts Are Usually Associated with System Dynamics Modeling (The MIT Press, Cambridge).Google Scholar
  • Gao R, Duru O (2020) Parsimonious fuzzy time series modelling. Expert Systems Appl. 156:113447.CrossrefGoogle Scholar
  • Gharehgozli A, Duru O, Bulut E (2018) Input data range optimization for freight rate forecasting using the rolling window testing procedure. Internat. J. Transport Econom. 45(3):393–413.Google Scholar
  • Greenwood R, Hanson SG (2014) Waves in ship prices and investment. Quart. J. Econom. 130(1):55–109.CrossrefGoogle Scholar
  • Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Internat. J. Forecasting 22(4):679–688.CrossrefGoogle Scholar
  • Jarque CM, Bera AK (1980) Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Econom. Lett. 6(3):255–259.CrossrefGoogle Scholar
  • Jeon JW, Yeo GT (2017) Study of the optimal timing of container ship orders considering the uncertain shipping environment. Asian J. Shipping Logist. 33(2):85–93.CrossrefGoogle Scholar
  • Jeon JW, Duru O, Yeo GT (2020) Modelling cyclic container freight index using system dynamics. Maritime Policy Management 47(3):287–303.CrossrefGoogle Scholar
  • Johansen S (1991) Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica 59(6):1551–1580.CrossrefGoogle Scholar
  • Kalman RE (1960) A new approach to linear filtering and prediction problems. J. Basic Engrg. 82(1):35–45.CrossrefGoogle Scholar
  • Keenan DM (1985) A Tukey nonadditivity-type test for time series nonlinearity. Biometrika 72:39–44.CrossrefGoogle Scholar
  • Kiiski T, Solakivi T, Töyli J, Ojala L (2018) Long-term dynamics of shipping and icebreaker capacity along the Northern Sea Route. Maritime Econom. Logist. 20(3):375–399.CrossrefGoogle Scholar
  • Koskinen MM, Hilmola OP (2005) Investment cycles in the newbuilding market of ice-strengthened oil tankers. Maritime Econom. Logist. 7(2):173–188.CrossrefGoogle Scholar
  • Koza DF, Desaulniers G, Ropke S (2020) Integrated liner shipping network design and scheduling. Transportation Sci. 54(2):512–533.LinkGoogle Scholar
  • Kroes JR, Chen Y, Mangiameli P (2013) Estimating demand for container freight service at the Port of Davisville. Interfaces 43(2):170–181.LinkGoogle Scholar
  • Leon H, Osman H, Georgy M, Elsaid M (2017) System dynamics approach for forecasting performance of construction projects. J. Management Engrg. 34(1):04017049.CrossrefGoogle Scholar
  • Ljung GM, Box GE (1978) On a measure of lack of fit in time series models. Biometrika 65(2):297–303.CrossrefGoogle Scholar
  • Luo M, Fan L, Liu L (2009) An econometric analysis for container shipping market. Maritime Policy Management 36(6):507–523.CrossrefGoogle Scholar
  • Lütkepohl H (1985) Comparison of criteria for estimating the order of a vector autoregressive process. J. Time Series Anal. 6(1):35–52.CrossrefGoogle Scholar
  • Mo L, Xie L, Jiang X, Teng G, Xu L, Xiao J (2018) GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries. Appl. Soft Comput. 62:478–490.CrossrefGoogle Scholar
  • Munim ZH, Schramm HJ (2017) Forecasting container shipping freight rates for the Far East–Northern Europe trade lane. Maritime Econom. Logist. 19(1):106–125.CrossrefGoogle Scholar
  • Munim ZH, Schramm HJ (2020) Forecasting container freight rates for major trade routes: A comparison of artificial neural networks and conventional models. Maritime Econom. Logist., ePub ahead of print April 2, https://doi.org/10.1057/s41278-020-00156-5.CrossrefGoogle Scholar
  • Nielsen P, Jiang L, Rytter NGM, Chen G (2014) An investigation of forecast horizon and observation fit’s influence on an econometric rate forecast model in the liner shipping industry. Maritime Policy Management 41(7):667–682.CrossrefGoogle Scholar
  • Niu M, Hu Y, Sun S, Liu Y (2018) A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting. Appl. Math. Mech. (English Ed.). 57:163–178.Google Scholar
  • Omer M, Mostashari A, Nilchiani R, Mansouri M (2012) A framework for assessing resiliency of maritime transportation systems. Maritime Policy Management 39(7):685–703.CrossrefGoogle Scholar
  • Pantuso G, Fagerholt K, Wallace SW (2015) Uncertainty in fleet renewal: A case from maritime transportation. Transportation Sci. 50(2):390–407.LinkGoogle Scholar
  • Park SI, Wang Y, Yeo GT, Ng AK (2014) System dynamics modeling for determining optimal ship sizes and types in coastal liner services. Asian J. Shipping Logist. 30(1):31–50.CrossrefGoogle Scholar
  • Phillips PC, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75(2):335–346.CrossrefGoogle Scholar
  • Randers J, Göluke U (2007) Forecasting turning points in shipping freight rates: Lessons from 30 years of practical effort. System Dynam. Rev. 23(2‐3):253–284.CrossrefGoogle Scholar
  • Rashed Y, Meersman H, Sys C, Van de Voorde E, Vanelslander T (2018) A combined approach to forecast container throughput demand: Scenarios for the Hamburg-Le Havre range of ports. Transportation Res. A: Policy Practice 117:127–141.CrossrefGoogle Scholar
  • Richardson GP (1995) Loop polarity, loop dominance, and the concept of dominant polarity. System Dynam. Rev. 11(1):67–88.CrossrefGoogle Scholar
  • Shepherd SP (2014) A review of system dynamics models applied in transportation. Transportmetrica B 2(2):83–105.Google Scholar
  • Sims CA (1980) Macroeconomics and reality. Econometrica 48(1):1–48.CrossrefGoogle Scholar
  • Slutzky E (1937) The summation of random causes as the source of cyclic processes. Econometrica 5:105–146.CrossrefGoogle Scholar
  • Sterman, J. D. (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World (McGraw-Hill, Boston).Google Scholar
  • Suryani E, Chou SY, Chen CH (2010) Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems Appl. 37(3):2324–2339.CrossrefGoogle Scholar
  • Tan R, Duru O, Thepsithar P (2020) Assessment of relative fuel cost for dual fuel marine engines along major Asian container shipping routes. Transportation Res. E: Logist. Transportation Rev. 140:102004.CrossrefGoogle Scholar
  • Tsay RS (1986) Nonlinearity test for time series. Biometrika 73:461–466.CrossrefGoogle Scholar
  • Veenstra AW, Franses PH (1997) A co-integration approach to forecasting freight rates in the dry bulk shipping sector. Transportation Res. A: Policy Practice 31(6):447–458.CrossrefGoogle Scholar
  • Vetitnev A, Kopyirin A, Kiseleva A (2016) System dynamics modelling and forecasting health tourism demand: The case of Russian resorts. Current Issues Tourism 19(7):618–623.CrossrefGoogle Scholar
  • Wold H (1938) A Study in the Analysis of Stationary Time Series (Almgrist & Wiksell, Stockholm).Google Scholar
  • Xia J, Li KX, Ma H, Xu Z (2015) Joint planning of fleet deployment, speed optimization, and cargo allocation for liner shipping. Transportation Sci. 49(4):922–938.LinkGoogle Scholar
  • Xie G, Zhang N, Wang S (2017) Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology. Transportation Res. E: Logist. Transportation Rev. 108:160–178.CrossrefGoogle Scholar
  • Yule GU (1926) Why do we sometimes get nonsense-correlations between time-series? A study in sampling and the nature of time-series. J. Roy. Statist. Soc. 89(1):1–63.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.