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Econometric estimation of second-hand shipping markets using panel data analysis

Author

Listed:
  • Nikolaos D. Geomelos

    (University of the Aegean, Department of Shipping, Trade and Transport)

  • Evangelos Xideas

    (University of the Aegean, Department of Shipping, Trade and Transport)

Abstract

Panel data analysis is becoming increasingly popular in shipping markets since it enables the employment of a wider source of variation which allows a more efficient estimation of a model’s parameters. This study applies an econometric analysis on a balanced panel data set of tankers’ second-hand prices for five different vessel types as cross-section identifiers. Empirical analysis investigates the existence of second-hand prices’ differentiation according to the vessel size using monthly observations for over a forty years time period. The key question concerns relationships among second-hand prices, spot rates and newbuilding prices and their dependence on whether vessel sizes experience low or higher rates of interdependence. Analysis focuses on the aspect of heterogeneity among variables, which is due to the effects of unobserved variables. The models estimate fixed and random effects and examine both cross section and time effects. Unit root and cointegration tests are performed in order to check for stationarity and for the existence of any longrun equilibrium relationships among variables. Also, Hausman test is adopted to test the existence of correlated random effects. Empirical results lead to conclusions and implications regarding the use of spot rates and newbuilding prices as intermediate means for the prediction of second-hand prices.

Suggested Citation

  • Nikolaos D. Geomelos & Evangelos Xideas, 2017. "Econometric estimation of second-hand shipping markets using panel data analysis," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 7-21, January-M.
  • Handle: RePEc:spd:journl:v:67:y:2016:i:1:p:7-21
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    File URL: https://spoudai.unipi.gr/index.php/spoudai/article/view/2569/2627/2569-3075-1-SM
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    References listed on IDEAS

    as
    1. S D Tsolakis & C Cridland & H E Haralambides, 2003. "Econometric Modelling of Second-hand Ship Prices," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 5(4), pages 347-377, December.
    2. Manolis G. Kavussanos & Amir H. Alizadeh, 2002. "Efficient pricing of ships in the dry bulk sector of the shipping industry," Maritime Policy & Management, Taylor & Francis Journals, vol. 29(3), pages 303-330.
    3. Albert W. Veenstra, 1999. "The term structure of ocean freight rates," Maritime Policy & Management, Taylor & Francis Journals, vol. 26(3), pages 279-293.
    4. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    5. Manolis Kavussanos, 1997. "The dynamics of time-varying volatilities in different size second-hand ship prices of the dry-cargo sector," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 433-443.
    6. Jostein Tvedt, 2003. "A new perspective on price dynamics of the dry bulk market," Maritime Policy & Management, Taylor & Francis Journals, vol. 30(3), pages 221-230, July.
    7. N.D. Geomelos & E. Xideas, 2014. "Forecasting spot prices in bulk shipping using multivariate and univariate models," Cogent Economics & Finance, Taylor & Francis Journals, vol. 2(1), pages 1-37, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Second-hand market; panel data analysis; cross-section analysis; fixed and random effects model;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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