| Abstract: | Forecasters are concerned with the accuracy of a forecast and whether the forecast can be modified to yield an improved performance. Theil has proposed statistics to measure forecast performance and to identify components of forecast error. However, the most commonly used of Theil's statistics have been shown to have serious shortcomings. This paper discusses Theil's decomposition of forecast error into bias, regression and disturbance proportions. Examples using price expectations and new housing starts data are given to show how decomposition suggests a linear correction procedure that may improve forecast accuracy. |
