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Forecast evaluation and improvement using theil's decomposition
Author(s) -
Ahlburg Dennis A.
Publication year - 1984
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.3980030313
Subject(s) - theil index , econometrics , decomposition , statistics , regression , measure (data warehouse) , forecast error , mathematics , economics , computer science , data mining , inequality , mathematical analysis , ecology , biology
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.