Forecasting Seasonal Time Series of Corporate Earnings: A Note
Date of Original Version
The purpose of this paper is to compare the accuracy of various models for forecasting time series of corporate earnings. Previous research indicated that user‐identified time series (ARIMA) models were less useful for forecasting corporate earnings than prespecified models of the Watts‐Griffin and Brown‐Rozeff type. In this study, these research results are disputed. Specifically, prespecified models did not produce models having smaller forecast errors in the statistical sense than did user‐identified models for the same time series data for the same time period. The user‐identified models are those selected by the prescriptive methods of Box‐Jenkins identification, estimation, and diagnostic testing. Furthermore, the magnitude of the forecasting error may be understated for the prespecified models indicating that Box‐Jenkins models may even be more useful than indicated by measures of forecast error. Copyright © 1990, Wiley Blackwell. All rights reserved
Publication Title, e.g., Journal
Jarrett, Jeffrey. "Forecasting Seasonal Time Series of Corporate Earnings: A Note." Decision Sciences 21, 4 (1990): 888-894. doi: 10.1111/j.1540-5915.1990.tb01257.x.