The international literature review reveals that the static approach dominates car ownership forecast. It is envisaged that the inclusion of the dynamic in car demand forecasting will yield fruitful results. Nevertheless, the use of dynamic approach in car demand forecasting is still limited due to heavy data requirement. Due to data constraint, there have been relatively few forecasting models that use the dynamic approach except some using aggregate time series methods. It is possible to forecast car demand using panel data model. One approach to circumvent the need for panel data is to construct pseudo panels from the cross sectional data. The pseudo-panel approach is a relatively new econometric approach to estimate dynamic demand models. A pseudopanel is an artificial panel based on (cohort) averages of repeated cross-sections. Extra restrictions are imposed on pseudo-panel data before one can treat it as actual panel data. By defining the cohorts one should pursue homogeneity within the cohorts and heterogeneity between the cohorts. In this way, one is able to overcome the deficiencies in both the static models and aggregate time series. In the current study, a pseudo panel dataset is constructed using the Family Expenditure Survey Data in the UK. The cohorts are defined on the basis of one common shared characteristic: year of birth of the head of the household. The birth cohort is defined in a fiveyear band. In total, the constructed pseudo panel has 254 observations, covering 19 years from 1982 to 2000. One common problem of pseudo panel model estimation is that the intercept term varies across cohorts, since the individual making up each cohort are not the same in every year. In this study, a major departure from previous studies is the estimation of non-linear models. Although non-linearity might not be a problem for the analytical purpose, it poses a serious problem for long term forecasting. A linear model cannot accommodate the saturation effect of car ownership and would result in the over-estimation of the car ownership in the long run. Tests of the various linear models constructed using the pseudo panel dataset reveal the presence of non-linearity. Various non-linear models have been estimated. The logistic models are directly estimated using Non-linear Least Square (NLSQ) methods. The saturation level is estimated using two different approaches: first directly using NLSQ and second using the DOGIT model. To aggregate up to get a forecast of car ownership, it is necessary to forecast the number of households and other economic/demographic variables (such as income and household size) for each cohort. As most data are available at person level, assumptions have been made regarding the household structure, which enables the conversion of person data to household data. Furthermore, there are cohorts unable to model (those with younger head of household and do not have enough observations in the Family Expenditure Survey). This problem can be solved by analysing the relationship between the cohort constants and assuming homogeneity between other regression parameters. For the covering abstract please see ITRD E135207.
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