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Recovering Measures of Advertising Carryover from Aggregate Data: The Role of the Firm's Decision Behavior

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  • Gary J. Russell

    (Vanderbilt University)

Abstract

Data interval bias, the biased estimation of advertising carryover in aggregate data, can be viewed as a misinterpretation of the aggregate advertising-sales relationship due to missing micro advertising data. This paper argues that if the researcher does not explicitly model the firm's advertising decisions, he will incorrectly interpolate the missing data and thereby allow the firm's decision behavior to influence inferences about advertising carryover. Drawing upon a general model of advertising decision behavior, the expected aggregate form of the Koyck relationship is developed and compared to existing bias correction methodologies. Although it is difficult to find any parsimonious procedure which is robust with respect to all types of decision behavior, allowing lagged advertising to enter the classical Koyck equation emerges as a simple method of obtaining a reasonable estimate of advertising carryover in aggregate data.

Suggested Citation

  • Gary J. Russell, 1988. "Recovering Measures of Advertising Carryover from Aggregate Data: The Role of the Firm's Decision Behavior," Marketing Science, INFORMS, vol. 7(3), pages 252-270.
    • Handle: RePEc:inm:ormksc:v:7:y:1988:i:3:p:252-270
    DOI: 10.1287/mksc.7.3.252
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    Cited by:

    1. Debanjan Mitra & Peter N. Golder, 2006. "How Does Objective Quality Affect Perceived Quality? Short-Term Effects, Long-Term Effects, and Asymmetries," Marketing Science, INFORMS, vol. 25(3), pages 230-247, 05-06.
    2. Franses, Philip Hans, 2006. "Forecasting in Marketing," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 18, pages 983-1012, Elsevier.
    3. Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2017. "Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 90-101.
    4. Kiygi Calli, M. & Weverbergh, M. & Franses, Ph.H.B.F., 2010. "To Aggregate or Not to Aggregate: Should decisions and models have the same frequency?," ERIM Report Series Research in Management ERS-2010-046-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    5. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.

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