Commercial market research firms provide information on advertising variables of interest, such as brand awareness or gross rating points, that are likely to contain measurement errors. This unreliability of measured variables induces bias in the estimated parameters of dynamic models of advertising. Consequently, advertisers either under- or overspend on advertising to maintain a desired level of brand awareness.

Monte Carlo studies show that the magnitude of bias can be serious when conventional estimation methods, such as ordinary least squares and errors in variables, are employed to obtain parameter estimates. Therefore, the authors have developed two new approaches that either reduce or eliminate parameter bias. Using these methods, advertisers can determine an unbiased optimal advertising budget, even if advertising variables are measured with error. The application of these methods to estimate the extent of measurement noise in empirical advertising data is illustrated.