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Modeling Purchases as Repeated Events

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  • Bijwaard, Govert E.
  • Franses, Philip Hans
  • Paap, Richard

Abstract

We put forward a statistical model for interpurchase times that takes into account all the current and past information available for all purchases as time continues to run along the calendar timescale. It delivers forecasts for the number of purchases in the next period and for the timing of the first and consecutive purchases. Purchase occasions are modeled in terms of a counting process, which counts the recurrent purchases for each household as they evolve over time. We show that formulating the problem as a counting process has many advantages, both theoretically and empirically. We illustrate our model for yogurt purchases and we highlight its useful managerial implications.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 24 (2006)
Issue (Month): (October)
Pages: 487-502

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Handle: RePEc:bes:jnlbes:v:24:y:2006:p:487-502

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References

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  1. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
  2. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-56, July.
  3. Chintagunta, Pradeep K & Prasad, Alok R, 1998. "An Empirical Investigation of the "Dynamic McFadden" Model of Purchase Timing and Brand Choice: Implications for Market Structure," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 2-12, January.
  4. Winkelmann, Rainer, 1995. "Duration Dependence and Dispersion in Count-Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 467-74, October.
  5. Gonul, F. & Srinivasan, K., 1993. "Consumer Purchase Behavior in a frequently Bought Product Category: Estimation Issues and Managerial Insights from a Hazard Function Model with Heterogeneity," University of Chicago - Economics Research Center 93-1, Chicago - Economics Research Center.
  6. Luc Duchateau & Paul Janssen & Iva Kezic & Catherine Fortpied, 2003. "Evolution of recurrent asthma event rate over time in frailty models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(3), pages 355-363.
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Cited by:
  1. Bijwaard, G.E., 2005. "Regularity in individual shopping trips: Implications for duration models in marketing," Econometric Institute Research Papers EI 2005-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Govert Bijwaard, 2014. "Multistate event history analysis with frailty," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(58), pages 1591-1620, May.
  3. Govert Bijwaard, 2012. "Unobserved Heterogeneity in Multiple-Spell Multiple-States Duration Models," Norface Discussion Paper Series 2012033, Norface Research Programme on Migration, Department of Economics, University College London.
  4. Franses, Ph.H.B.F., 2004. "Forecasting in marketing," Econometric Institute Research Papers EI 2004-40, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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