Time Interval Bias: Its Impact on Advertising Budgeting

Abstract

Abstract This paper considers what happens when two key parameters in advertising budgeting models (immediate and lagged effects of advertising) are estimated without recognition of time interval (or temporal aggregation) bias. It is demonstrated that careful attention should be paid to whether annual, biannual or quarterly advertising-sales data are used in estimating these key budgeting parameters, otherwise the measurement bias in the key budgeting parameters is likely to be severe. Most importantly, this measurement bias is shown to impact substantially the managerial accuracy and usefulness of dynamic advertising budgeting models.