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Temporal Aggregation, the Data Interval Bias, and Empirical Estimation of Bimonthly Relations from Annual Data

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  • Frank M. Bass

    (University of Texas, Dallas)

  • Robert P. Leone

    (University of Texas, Austin)

Abstract

In an important study reviewing the literature on econometric studies of the relationship between advertising and sales Clarke (Clarke, Darral G. 1976. Econometric measurement of the duration of advertising effects on sales. J. Marketing Res. 13 (November) 345--357.) concluded that the implied duration interval of the effects of advertising on sales were too long when the studies used annual data. A theoretical explanation is provided here for the observation of parameter estimates which vary with the data interval employed in the analysis. Parameter estimates are developed on the basis of data aggregated at various levels of temporal aggregation and compared with theoretical values. It is demonstrated that it is possible to recover bimonthly parameters when only annual data are available.

Suggested Citation

  • Frank M. Bass & Robert P. Leone, 1983. "Temporal Aggregation, the Data Interval Bias, and Empirical Estimation of Bimonthly Relations from Annual Data," Management Science, INFORMS, vol. 29(1), pages 1-11, January.
    • Handle: RePEc:inm:ormnsc:v:29:y:1983:i:1:p:1-11
    DOI: 10.1287/mnsc.29.1.1
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    Cited by:

    1. David Swanson & George Hough, 2012. "An Evaluation of Persons per Household (PPH) Estimates Generated by the American Community Survey: A Demographic Perspective," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 31(2), pages 235-266, April.
    2. Seldom, Barry J. & Jung, Chulho, 1995. "The length of the effect of aggregate advertising on aggregate consumption," Economics Letters, Elsevier, vol. 48(2), pages 207-211, May.
    3. Seldon, Barry J. & Jewell, R. Todd & O'Brien, Daniel M., 2000. "Media substitution and economies of scale in advertising," International Journal of Industrial Organization, Elsevier, vol. 18(8), pages 1153-1180, December.
    4. Jon P. Nelson, 1999. "Broadcast Advertising and U.S. Demand for Alcoholic Beverages," Southern Economic Journal, John Wiley & Sons, vol. 65(4), pages 774-790, April.
    5. Zizhuo Wang & Chaolin Yang & Hongsong Yuan & Yaowu Zhang, 2021. "Aggregation Bias in Estimating Log‐Log Demand Function," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3906-3922, November.
    6. Mingyung Kim & Eric T. Bradlow & Raghuram Iyengar, 2022. "Selecting Data Granularity and Model Specification Using the Scaled Power Likelihood with Multiple Weights," Marketing Science, INFORMS, vol. 41(4), pages 848-866, July.
    7. 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.
    8. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    9. Kumar, V. & Leone, Robert P. & Gaskins, John N., 1995. "Aggregate and disaggregate sector forecasting using consumer confidence measures," International Journal of Forecasting, Elsevier, vol. 11(3), pages 361-377, September.
    10. 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.
    11. Ralph Breuer & Malte Brettel & Andreas Engelen, 2011. "Incorporating long-term effects in determining the effectiveness of different types of online advertising," Marketing Letters, Springer, vol. 22(4), pages 327-340, November.
    12. Breuer, Ralph & Brettel, Malte, 2012. "Short- and Long-term Effects of Online Advertising: Differences between New and Existing Customers," Journal of Interactive Marketing, Elsevier, vol. 26(3), pages 155-166.
    13. Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.
    14. Caroline Elliott, 2001. "A Cointegration Analysis of Advertising and Sales Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 18(4), pages 417-426, June.
    15. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
    16. Nicole Jonker & Mirjam Plooij & Johan Verburg, 2017. "Did a Public Campaign Influence Debit Card Usage? Evidence from the Netherlands," Journal of Financial Services Research, Springer;Western Finance Association, vol. 52(1), pages 89-121, October.
    17. 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.
    18. Neelotpaul Banerjee & Somroop Siddhanta, 2015. "An Empirical Investigation on the Impact of Marketing Communication Expenditure on Firms’ Profitability: Evidence from India," Global Business Review, International Management Institute, vol. 16(4), pages 609-622, August.
    19. Chanjin Chung & Harry M. Kaiser, 2000. "Determinants of temporal variations in generic advertising effectiveness," Agribusiness, John Wiley & Sons, Ltd., vol. 16(2), pages 197-214.
    20. Nelson, Jon P., 2001. "Alcohol Advertising and Advertising Bans: A Survey of Research Methods, Results, and Policy Implications," Working Papers 7-01-2, Pennsylvania State University, Department of Economics.

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