Combining Annual Econometric Forecasts with Quarterly ARIMA Forecasts: A Heuristic Approach

Data limitations often limit the time framework in which agricultural commodities are modeled and prices forecasted. Our research provides a technique to alleviate this constraint. By combining an annual econometric model with a quarterly ARIMA model, quarterly forecasts can be made which utilize the theoretical and structural foundations in econometric modeling. The topic of price forecasting has long been an area of interest to economists. Though much has been written on alter-native forecasting methods (e.g., Brandt and Bessler; Granger and Newbold; Leut-hold et al.; Helmers and Held; Pierce and Porter), any one technique is not satisfac-tory for all situations. Often the researcher is faced with a situation requiring fore-casts with data available only at a higher level of temporal aggregation. This paper addresses this problem by suggesting a technique of forecasting a quarterly price variable with data available for most ex-planatory variables only on an annual ba-sis. The objective of this study is to evaluate an ad hoc procedure of combining annual forecasts of alfalfa hay prices from an econometric model with quarterly alfalfa hay price forecasts from an ARIMA mod-el. In this manner, the benefits of both modeling methodologies are incorporat-ed, forecasting frequency is increased, and forecasting accuracy is improved