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Sales revenues: Time‐series properties and predictions
Author(s) -
AbdelKhalik A. R.,
ElSheshai K. M.
Publication year - 1983
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.3980020405
Subject(s) - autoregressive integrated moving average , econometrics , griffin , revenue , series (stratigraphy) , earnings , time series , economics , computer science , statistics , mathematics , finance , geography , paleontology , archaeology , biology
This paper compares the predictive ability of ARIMA models in forecasting sales revenue. Comparisons were made at both industry and firm levels. With respect to the form of the ARIMA model, a parsimonious model of the form (0, 1, 1) (0, 1, 1) was identified most frequently for firms and industries. This model was identified previously by Griffin and Watts for the earnings series, and by Moriarty and Adams for the sales series. As a parsimonious model, its predictive accuracy was quite good. However, predictive accuracy was also found to be a function of the industry. Out of the eleven industry classifications, ‘metals’ had the lowest predictive accuracy using both firmspecific and industry‐specific ARIMA models.