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FORECAST MODIFICATION BASED UPON RESIDUAL ANALYSIS: A CASE STUDY OF CHECK VOLUME ESTIMATION
Author(s) -
Mabert Vincent A.
Publication year - 1978
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1978.tb01385.x
Subject(s) - exponential smoothing , residual , smoothing , econometrics , regression , computer science , simple linear regression , statistics , regression analysis , linear regression , forecast error , estimation , mathematics , algorithm , economics , management
The inability to identify all causal variables in a linear regression demand model may result in serial correlation which is generally considered undesirable; but it may be possible to take advantage of such an event. This case study, based upon Chemical Bank of New York, investigates the use of simple and exponential smoothing for modifying initial estimates from a regression model by using prior forecast error patterns to obtain better forecasts. The smoothing approaches are combined with a regression model to test for improved performance in predicting daily check volumes.