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A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables

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  • Richard Paap

    (Rotterdam Institute for Business Economic Studies, Erasmus University Rotterdam, PO Box 1738, NL-3000, DR Rotterdam, The Netherlands)

  • Philip Hans Franses

    (Econometric Institute and Department of Marketing and Organization, Erasmus University Rotterdam, The Netherlands)

Abstract

In this paper we propose a dynamic multinomial probit model in order to estimate the long-run and short- run effects of marketing mix variables on brand choice. The latent variables, which contain the unobserved perceived utilities, follow a first-order vector error correction autoregressive process of order 1 with current and lagged explanatory variables. The unrestricted autoregressive parameter matrix concerns the intertemporal correlation in perceived utilities of households over purchase occasions and indicates the persistence in brand choice. As explanatory variables we consider relative prices and promotional activities like feature and display. An important and novel feature of our model is that it allows for different long-run and short-run effects of promotional activities, thereby extending the models that are currently available in the literature. Additionally, to account for different base preferences for brands across households, we allow for consumer heterogeneity. Our application concerns a panel of households choosing among several brands of a FMCG. Our estimated model turns out to be an improvement over a static model and over a model with only short-run effects, in terms of in-sample fit and out-of-sample forecasts. Copyright © 2000 John Wiley & Sons, Ltd.

Suggested Citation

  • Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
    • Handle: RePEc:jae:japmet:v:15:y:2000:i:6:p:717-744
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    Cited by:

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    3. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2020. "Inference on Semiparametric Multinomial Response Models," Discussion Papers Series 627, School of Economics, University of Queensland, Australia.
    4. Can, Vo Van, 2013. "Estimation of travel mode choice for domestic tourists to Nha Trang using the multinomial probit model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 149-159.
    5. Fok, Dennis & Paap, Richard & Franses, Philip Hans, 2012. "Modeling dynamic effects of promotion on interpurchase times," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3055-3069.
    6. Xiaokun Wang & Kara M. Kockelman, 2009. "Baysian Inference For Ordered Response Data With A Dynamic Spatial‐Ordered Probit Model," Journal of Regional Science, Wiley Blackwell, vol. 49(5), pages 877-913, December.
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    8. Fok, D. & Paap, R. & Franses, Ph.H.B.F., 2003. "Modeling Dynamic Effects of the Marketing Mix on Market Shares," ERIM Report Series Research in Management ERS-2003-044-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.
    9. Choudhury, Charisma F. & Yang, Lang & de Abreu e Silva, João & Ben-Akiva, Moshe, 2018. "Modelling preferences for smart modes and services: A case study in Lisbon," Transportation Research Part A: Policy and Practice, Elsevier, vol. 115(C), pages 15-31.
    10. Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po, 2021. "Forecasting week-to-week television ratings using reduced-form and structural dynamic models," International Journal of Forecasting, Elsevier, vol. 37(1), pages 302-321.
    11. Shakeeb Khan & Fu Ouyang & Elie Tamer, 2019. "Inference on Semiparametric Multinomial Response Models," Boston College Working Papers in Economics 980, Boston College Department of Economics.
    12. Richard T. Carson & Derrick H. Sun & Yixiao Sun, 2024. "Random Utility Models with Skewed Random Components: the Smallest versus Largest Extreme Value Distribution," Papers 2405.08222, arXiv.org, revised May 2024.
    13. Michael P. Keane, 2013. "Panel data discrete choice models of consumer demand," Economics Papers 2013-W08, Economics Group, Nuffield College, University of Oxford.
    14. van Heerde, H.J. & Helsen, K. & Dekimpe, M.G., 2005. "Managing Product-Harm Crises," ERIM Report Series Research in Management ERS-2005-044-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.
    15. Lilach Nachum & Srilata Zaheer & Shulamith Gross, 2008. "Does It Matter Where Countries Are? Proximity to Knowledge, Markets and Resources, and MNE Location Choices," Management Science, INFORMS, vol. 54(7), pages 1252-1265, July.
    16. Meng, Ting & Klepacka, Anna M. & Florkowski, Wojciech J. & Braman, Kristine, 2015. "What drives an environmental horticultural firm to start recycling plastics? Results of a Georgia survey," Resources, Conservation & Recycling, Elsevier, vol. 102(C), pages 1-8.
    17. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    18. Fok, D. & Franses, Ph.H.B.F. & Paap, R., 2001. "Econometric Analysis of the Market Share Attraction Model," ERIM Report Series Research in Management ERS-2001-25-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.
    19. Kiygi Calli, M. & Weverbergh, M. & Franses, Ph.H.B.F., 2008. "Modeling the Effectiveness of Hourly Direct-Response Radio Commercials," ERIM Report Series Research in Management ERS-2008-019-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.
    20. Fok, D. & Horváth, C. & Paap, R. & Franses, Ph.H.B.F., 2004. "A hierarchical Bayes error correction model to explain dynamic effects," Econometric Institute Research Papers EI 2004-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    21. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.
    22. M. Tolga Akçura & Füsun F. Gönül & Elina Petrova, 2004. "Consumer Learning and Brand Valuation: An Application on Over-the-Counter Drugs," Marketing Science, INFORMS, vol. 23(1), pages 156-169, April.
    23. Korkmaz, E. & Fok, D. & Kuik, R., 2014. "The Need for Market Segmentation in Buy-Till-You-Defect Models," ERIM Report Series Research in Management ERS-2014-006-LIS, 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.
    24. Harald Van Heerde & Kristiaan Helsen & Marnik G. Dekimpe, 2007. "The Impact of a Product-Harm Crisis on Marketing Effectiveness," Marketing Science, INFORMS, vol. 26(2), pages 230-245, 03-04.

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