Traffic Sensitivity of Long-term Regional Growth Forecasts

Long-term forecasting is a big challenge for the regional modelling, since only a few years of panel data are available on a regional basis. Furthermore, traffic dependent models must be developed to explore the sensitivity of travelling times on the socio-demographic variables of a region. Using the sophisticated model choice procedure BMA (Bayesian model averaging, see Raftery et al. 1997) for the entire regional data set we have successfully reduced the pool of variables and we are able concentrate solely on demo-economic variables with traffic related backgrounds. We consider two types of forecasts (with or without country-wise adjustments) and 2 railway travel time (TT) scenarios: scenario 1 assumes that all presently planed projects (i.e. for the decade 2000-2010) will be realized according to the national traffic plans. Scenario 2 assumes railway investments that will remove all in the year 2000 known bottlenecks in the decade from 2010 to 2020. We will forecast the main economic characteristics of a region, namely the GDP growth rates, the employment rate and the population growth rate. The population growth rates forecast are compared with middle scenario ÖROK forecasts in the appendix, and surprisingly we find only small differences (the maximum is 0.5%) between this long-term demographic projection method (based on 100 age classes and constant fertility assumptions) and our Panel base forecast. The comparison is shown for the SIC regions in the appendix B. In the remaining Section 1 we introduce the regional modelling approach and in Section 2 describe the traffic dependent GDP growth model. We define all the “spatial” related regressor variables that pick up the space and traffic interactions