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CHAPTER 2 FREIGHT FORECASTING MODELSINTRODUCTIONIn this study, forecasts of containerised and non-containerised trade, cruise passengernumbers, and the movement of freight, cruise and other ships are developed usingeconometric models. A detailed discussion on the models and their estimatedparameters is presented in the later sections of this chapter.As mentioned in Chapter 1, the forecasts of containerised and non-containerised tradeand freight ship movements are developed at the national and port level. However, theforecasts of cruise passenger numbers and the movement of cruise and other ships couldnot be developed at the port level, mainly due to the lack of long time-series port leveldata on cruise passenger numbers and the movement of cruise and other ships. Theforecasts are` developed at the national level only.Since the five main city ports (i.e. Brisbane, Sydney, Melbourne, Adelaide andFremantle) dominate Australia’s containerised trade, accounting for 89.9 per cent of thetotal containerised trade, their growth rate forecasts are used to derive forecasts ofcontainer and container ship movements at the national level. This approach has beenused following the predictive-accuracy of the port level models relative to the nationallevel models, and to maintain consistency in port and national level forecasts. Theapproach was not found appropriate to forecast non-containerised trade and noncontainership movements at the national level. This is because the five main city portsaccount for only 11.2 per cent of the total non-containerised trade and, hence, do notdominate Australia’s non-containerised trade. The national level forecasts of noncontainerisedtrade and the movement of non-container ships are developed using thenational level econometric models.In this study, the term ‘all ports’ is used to refer to the total of all Australian ports (thatis, national level).Forecasts for other ports are derived by subtracting the total forecasts for the five maincity ports from the national level forecasts.The econometric models have been chosen to forecast container and ship movements atthe port level, because they are relatively better than time trend or univariate time-seriesmodels in the sense that the models can accommodate several explanatory variables toanalyse their influence on container and ship movements. Since the models are specifiedin a double logarithmic linear functional form, they are easy to estimate, provide5