Abstract
We consider changes in ownership of commercial shipping vessels from an event history perspective. Each change in ownership can be influenced by the properties of the vessel itself, its age and history to date, the characteristics of both the seller and the buyer, and time-varying market conditions. Similar factors can affect the process of deciding when to scrap the vessel as no longer being economically viable. We consider a multi-state approach in which states are defined by the owning companies, a sale marks a transition, and scrapping of the vessel corresponds to moving to an absorbing state. We propose a dual frailty model that attempts to capture unexplained heterogeneity in the data, with one frailty term for the seller and one for the buyer. We describe a Monte Carlo Markov chain estimation procedure and verify its accuracy through simulations. We investigate the consequences of mistakenly ignoring frailty in these circumstances. We compare results with and without the inclusion of frailty.
Highlights
We consider an event history approach to the analysis of ownership duration data, with focus on maritime transport
Suggest that an examination of how event history analysis could be applied in this field is long overdue and that any such analysis should accommodate the dual interpretation of an event, as explained in the first paragraph
The diagram will be explained and discussed further but it is clear that the relationship between buyers and sellers is complex and there is potential for significant heterogeneity in behaviour. We can model such heterogeneity with random effects, an approach that was first introduced by Beard (1959) with the alternative term frailty coined by Vaupel et al (1979) 20 years later
Summary
We consider an event history approach to the analysis of ownership duration data, with focus on maritime transport. A first approach to such an event history analysis of vessel ownership is usually to examine covariates in an attempt to explain the variation in the time taken until the specified event occurs. The diagram will be explained and discussed further but it is clear that the relationship between buyers and sellers is complex and there is potential for significant heterogeneity in behaviour We can model such heterogeneity with random effects, an approach that was first introduced by Beard (1959) with the alternative term frailty coined by Vaupel et al (1979) 20 years later. Be confident that we have captured all of the effects that may influence the sale of a vessel so we look to include separate frailty terms for both the selling company and the buying company, an approach we call dual frailty and explain as follows.
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