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Marketing response and temporal aggregation

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  • Philip Hans Franses

    (Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam)

Abstract

This paper deals with inferring key parameters on marketing response at a true high frequency while data are partly or fully available only at a lower frequency aggregate levels. The familiar Koyck model turns out to be very useful for this purpose. Assuming this model for the high-frequency data makes it possible to infer the high-frequency parameters from modified Koyck type models when lower frequency data are available. This means that inference using the Koyck model is robust to temporal aggregation.

Suggested Citation

  • Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.
    • Handle: RePEc:pal:jmarka:v:9:y:2021:i:2:d:10.1057_s41270-020-00102-7
    DOI: 10.1057/s41270-020-00102-7
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    References listed on IDEAS

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    1. Andreou, Elena & Ghysels, Eric & Kourtellos, Andros, 2010. "Regression models with mixed sampling frequencies," Journal of Econometrics, Elsevier, vol. 158(2), pages 246-261, October.
    2. Herrington, J. Duncan & Dempsey, William A., 2005. "Comparing the Current Effects and Carryover of National-, Regional-, and Local-Sponsor Advertising," Journal of Advertising Research, Cambridge University Press, vol. 45(1), pages 60-72, March.
    3. 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.
    4. Sood, Ashish & Kappe, Eelco & Stremersch, Stefan, 2014. "The commercial contribution of clinical studies for pharmaceutical drugs," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 65-77.
    5. Seshadri Tirunillai & Gerard J. Tellis, 2012. "Does Chatter Really Matter? Dynamics of User-Generated Content and Stock Performance," Marketing Science, INFORMS, vol. 31(2), pages 198-215, March.
    6. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    7. Kiygi Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2012. "The effectiveness of high-frequency direct-response commercials," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 98-109.
    8. Franses, Philip Hans & van Oest, Rutger, 2007. "On the econometrics of the geometric lag model," Economics Letters, Elsevier, vol. 95(2), pages 291-296, May.
    9. Ketan Mulchandani & Kalyani Mulchandani & Rekha Attri, 2019. "An assessment of advertising effectiveness of Indian banks using Koyck model," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 16(4), pages 498-512, March.
    10. 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.
    11. 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.
    12. Antonio G. Chessa & Jaap M. J. Murre, 2007. "A Neurocognitive Model of Advertisement Content and Brand Name Recall," Marketing Science, INFORMS, vol. 26(1), pages 130-141, 01-02.
    13. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.
    14. Yoo, Boonghee & Mandhachitara, Rujirutana, 2003. "Estimating Advertising Effects on Sales in a Competitive Setting," Journal of Advertising Research, Cambridge University Press, vol. 43(3), pages 310-321, September.
    15. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    16. Robert P. Leone, 1995. "Generalizing What Is Known About Temporal Aggregation and Advertising Carryover," Marketing Science, INFORMS, vol. 14(3_supplem), pages 141-150.
    17. Lizhen Xu & Jason A. Duan & Andrew Whinston, 2014. "Path to Purchase: A Mutually Exciting Point Process Model for Online Advertising and Conversion," Management Science, INFORMS, vol. 60(6), pages 1392-1412, June.
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