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Traditional Time-Series Models

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Book cover Advanced Methods for Modeling Markets

Part of the book series: International Series in Quantitative Marketing ((ISQM))

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Abstract

Marketing and performance data often include measures repeated (typically at equally spaced intervals) over time. In Chap. 2 of Vol. I, we captured market dynamics with lagged effects for predictor variables and distributed lags on the criterion variable, y. In this chapter, we refine and extend this treatment of market dynamics by means of time-series models. Time-series models are uniquely suited to capture the time dependence of both a criterion variable (e.g. sales performance) and predictor variables (e.g. marketing actions, online consumer behavior metrics), how they relate to each other over time.

Parts of this chapter are based on Hanssens et al. (2001) and Sect. 17.8 in Leeflang et al. (2000). We like to thank Michel Wedel for his efforts to compose this section in Leeflang et al. (2000).

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Notes

  1. 1.

    See, for example, Chap. 9.

  2. 2.

    See Sect. 5.4.3 in Vol. I.

  3. 3.

    This example is based on an unpublished study by Leeflang and Wedel. See Leeflang et al. (2000, pp. 472–473).

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Authors and Affiliations

  1. Department of Marketing, Northeastern University, Boston, USA

    Koen H. Pauwels

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  1. Koen H. Pauwels
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Correspondence to Koen H. Pauwels .

Editor information

Editors and Affiliations

  1. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Peter S. H. Leeflang

  2. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Jaap E. Wieringa

  3. Department of Marketing, University of Groningen, Groningen, The Netherlands

    Tammo H.A Bijmolt

  4. Department of Marketing, D’Amore-McKim School of Business, Northeastern University, Boston, USA

    Koen H. Pauwels

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Pauwels, K.H. (2017). Traditional Time-Series Models. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds) Advanced Methods for Modeling Markets. International Series in Quantitative Marketing. Springer, Cham. https://doi.org/10.1007/978-3-319-53469-5_3

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