This paper evaluates the appropriateness of autoregressive integrated moving average (ARIMA) time-series models for forecasting in information-scarce environments. Such environments are defined and a specific transportation sector example examined. It is shown that ARIMA models adequately characterize economic activity in this example. ARIMA model forecasts are seen to dominate alternative models, and to provide information required for market efficiency. Specific attention is focused on a procedure for isolating potential model misspecification and on the need for post-sample forecast quality analysis.
- ARIMA models
- Forecast error analysis
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty