Time Series Forecasting for Demand of Vehicles

Student Name: Krishna Murugan

Certain organization was interested in predicting the demand for their freight vehicles in the European market for transportation of goods. Past monthly data on demand was available for this purpose. To enahance the forecasting model observations were collected on several monthly economic indicator series as well, which include variables like consumer price index, producere price index, production volume index, retail series index, volume of trade, share price index, commercial bank lending rates, employment index, enerrgy figures etc. Next several approaches were considered for building an econometric forecasting model. First a simple ARIMA model was built for the vehicle demand series with no co-variate. Next a co-integration model was developed for the vehicle demand series along with the individual covariates as they were observed. Finally the independent series were factor analyzed, after a preliminary prinicipal component analsis, to remove multicollinearity and dimension reduction (which interestingly brought the oriniginal list of some ten odd independent variable to only two), and then a cointegration model for the vehicle demand series was built with the two factor score series. The performance of these models was then compared using AIC and their prediction accuracy in out of the sample period. The co-integration model with the factor scores turned out to be the one with the best performance, while the simple ARIMA model of the vehicle demand series was the worst.