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Forecasting in Marketing

In: Handbook of Economic Forecasting

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  • Franses, Philip Hans

Abstract

With the advent of advanced data collection techniques, there is an increased interest in using econometric models to support decisions in marketing. Due to the sometimes specific nature of variables in marketing, the discipline uses econometric models that are rarely, if ever, used elsewhere. This chapter deals with techniques to derive forecasts from these models. Due to the intrinsic non-linear nature of these models, these techniques draw heavily on simulation techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecofch:1-18
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    1. Bijwaard, Govert E. & Franses, Philip Hans & Paap, Richard, 2006. "Modeling Purchases as Repeated Events," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 487-502, October.
    2. Franses, Ph.H.B.F. & Vroomen, B.L.K., 2003. "Estimating duration intervals," ERIM Report Series Research in Management ERS-2003-031-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.
    3. Boswijk, H. Peter & Franses, Philip Hans, 2005. "On the Econometrics of the Bass Diffusion Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 255-268, July.
    4. Arino, Miguel A. & Franses, Philip Hans, 2000. "Forecasting the levels of vector autoregressive log-transformed time series," International Journal of Forecasting, Elsevier, vol. 16(1), pages 111-116.
    5. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    6. Seetharaman, P B & Chintagunta, Pradeep K, 2003. "The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 368-382, July.
    7. Rutger van Oest & Richard Paap & Philip Hans Franses, 2002. "A Joint Framework for Category Purchase and Consumption Behavior," Tinbergen Institute Discussion Papers 02-124/4, Tinbergen Institute.
    8. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    9. Franses, Ph.H.B.F. & van Oest, R.D., 2004. "On the econometrics of the Koyck model," Econometric Institute Research Papers EI 2004-07, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Hansen, Bruce E, 1996. "Inference When a Nuisance Parameter Is Not Identified under the Null Hypothesis," Econometrica, Econometric Society, vol. 64(2), pages 413-430, March.
    11. Kumar, V., 1994. "Forecasting performance of market share models: an assessment, additional insights, and guidelines," International Journal of Forecasting, Elsevier, vol. 10(2), pages 295-312, September.
    12. Klapper, Daniel & Herwartz, Helmut, 2000. "Forecasting market share using predicted values of competitive behavior: further empirical results," International Journal of Forecasting, Elsevier, vol. 16(3), pages 399-421.
    13. 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.
    14. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
    15. 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.
    16. van Nierop, J.E.M. & Fok, D. & Franses, Ph.H.B.F., 2002. "Sales Models For Many Items Using Attribute Data," ERIM Report Series Research in Management ERS-2002-65-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.
    17. Robert P. Leone, 1995. "Generalizing What Is Known About Temporal Aggregation and Advertising Carryover," Marketing Science, INFORMS, vol. 14(3_supplem), pages 141-150.
    18. Paap, Richard & Franses, Philip Hans & van Dijk, Dick, 2005. "Does Africa grow slower than Asia, Latin America and the Middle East? Evidence from a new data-based classification method," Journal of Development Economics, Elsevier, vol. 77(2), pages 553-570, August.
    19. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, September.
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    Cited by:

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    3. Pauwels, Koen & Neslin, Scott A., 2015. "Building With Bricks and Mortar: The Revenue Impact of Opening Physical Stores in a Multichannel Environment," Journal of Retailing, Elsevier, vol. 91(2), pages 182-197.
    4. Marusia Ivanova, 2007. "Genesis and Evolution of Market Share Predictive Models," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 117-148.
    5. Alexander Faehnle & Mariangela Guidolin, 2021. "Dynamic Pricing Recognition on E-Commerce Platforms with VAR Processes," Forecasting, MDPI, vol. 3(1), pages 1-15, March.
    6. Rafael Barreiros Porto & Nolah Schutte da Rocha Lima, 2015. "Nonlinear Impact of the Marketing Mix on Brand Sales Performance," Brazilian Business Review, Fucape Business School, vol. 12(5), pages 57-77, September.

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