Abstract
In this study, we postulate that forecasters desire to improve their performance by studying their past forecasting errors. To improve performance, forecasters may measure their past mistakes and revise their forecasts by forecast revision techniques. In an empirical test, forecasts of fifty firms' EPS were prepared by seven forecasting models. The initial forecasts were corrected by the Theil optimal linear correction technique and a Bayesian revision adjustment. Results indicate that the optimal linear correction technique is superior to not correcting for past forecast error.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
