QUARTERLY FORECASTING OF RAILROAD GRAIN CARLOADS

The participants in the grain logistics system need forecasts of railroad grain carloads. Although forecasting studies have been conducted for virtually every mode, no forecasting studies of quarterly railroad grain transportation have been published so the intent of this paper is to remedy that omission. The objectives of the paper are (1) specify a U.S. quarterly railroad grain transportation forecasting model, and (2) empirically estimate the model developed in the first objective. Given the objectives of this study, the selection of explanatory variables requires that they have a theoretical relationship to railroad grain transportation supply and/or demand and that the data for the explanatory variables is published in quarterly frequency. However, there are relatively few potential explanatory variables that are published quarterly and those that are available in quarterly frequency appear to have weak correlation to quarterly railroad grain carloadings. This result indicates that the economic process generating quarterly railroad grain carloadings is quite complex and very difficult to model with regression techniques. Given this problem and the focus on short run forecasting, a time series model was employed to forecast quarterly railroad grain carloadings. An AR(4) time series model was estimated using the Maximum Likelihood estimation procedure for the 1987:4-1997:4 period. The actual railroad grain carloadings for this period were compared to the forecast carloadings generated by the time series model. For 92 percent of the 37 quarters the percentage difference between the actual and forecast values was 10 percent or less. Of the 9 annual observations, the percent difference between the actual and forecast value was less than 2.6 percent for 8 of the 9 years.