IDEAS home Printed from https://ideas.repec.org/p/ems/eureir/1631.html
   My bibliography  Save this paper

Forecasting in marketing

Author

Listed:
  • Franses, Ph.H.B.F.

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 heavliy on simulation techniques.

Suggested Citation

  • 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.
    • Handle: RePEc:ems:eureir:1631
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/1631/ei200440.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Fok, Dennis & Franses, Philip Hans, 2001. "Forecasting market shares from models for sales," International Journal of Forecasting, Elsevier, vol. 17(1), pages 121-128.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Franses,Philip Hans & Paap,Richard, 2010. "Quantitative Models in Marketing Research," Cambridge Books, Cambridge University Press, number 9780521143653, January.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    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. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    18. 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.
    19. Robert P. Leone, 1995. "Generalizing What Is Known About Temporal Aggregation and Advertising Carryover," Marketing Science, INFORMS, vol. 14(3_supplem), pages 141-150.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. Patricia Chelley-Steeley & James Steeley, 2005. "The leverage effect in the UK stock market," Applied Financial Economics, Taylor & Francis Journals, vol. 15(6), pages 409-423.
    4. Appel, Gil & Libai, Barak & Muller, Eitan, 2018. "On the monetary impact of fashion design piracy," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 591-610.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521520911, January.
    2. 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.
    3. 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.
    4. 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.
    5. Candelon, Bertrand & Lieb, Lenard, 2013. "Fiscal policy in good and bad times," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2679-2694.
    6. Albert J.F. Yang & William N. Trumbull & Chin Wei Yang & Bwo‐Nung Huang, 2011. "On The Relationship Between Military Expenditure, Threat, And Economic Growth: A Nonlinear Approach," Defence and Peace Economics, Taylor & Francis Journals, vol. 22(4), pages 449-457, April.
    7. Philip Hans Franses & Dick van Dijk, 2006. "A simple test for PPP among traded goods," Applied Financial Economics, Taylor & Francis Journals, vol. 16(1-2), pages 19-27.
    8. Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    9. Chung‐Hua Shen & Hsing‐Hua Hsu, 2022. "The determinants of Asian banking crises—Application of the panel threshold logit model," International Review of Finance, International Review of Finance Ltd., vol. 22(1), pages 248-277, March.
    10. Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
    11. Munehisa Kasuya, 2003. "Regime-Switching Approach to Monetary Policy Effects: Empirical Studies using a Smooth Transition Vector Autoregressive Model," Bank of Japan Working Paper Series Research and Statistics D, Bank of Japan.
    12. Che, Chou Ming, 2013. "Panel threshold analysis of Taiwan's outbound visitors," Economic Modelling, Elsevier, vol. 33(C), pages 787-793.
    13. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    14. Sokbae Lee & Myung Hwan Seo & Youngki Shin, 2017. "Correction," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 883-883, April.
    15. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    16. Kadilli, Anjeza & Krishnakumar, Jaya, 2022. "Smooth Transition Simultaneous Equation Models," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    17. Antonio Diez De Los Rios & René Garcia, 2011. "Assessing and valuing the nonlinear structure of hedge fund returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(2), pages 193-212, March.
    18. Donald W.K. Andrews & Werner Ploberger, 1994. "Testing for Serial Correlation Against an ARMA(1,1) Process," Cowles Foundation Discussion Papers 1077, Cowles Foundation for Research in Economics, Yale University.
    19. Buraschi, Andrea & Jiltsov, Alexei, 2005. "Inflation risk premia and the expectations hypothesis," Journal of Financial Economics, Elsevier, vol. 75(2), pages 429-490, February.
    20. Burcu Kapar & William Pouliot, 2013. "Multiple Change-Point Detection in Linear Regression Models via U-Statistic Type Processes," Discussion Papers 13-13, Department of Economics, University of Birmingham.

    More about this item

    Keywords

    Bass model; Koyck model; attraction model; forecasting; marketing; unobserved heterogeneity;
    All these keywords.

    JEL classification:

    • B0 - Schools of Economic Thought and Methodology - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ems:eureir:1631. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.