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Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products

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  • Leonard J. Paas
  • Jeroen K. Vermunt
  • Tammo H. A. Bijmolt

Abstract

Summary. The paper demonstrates application of the latent Markov model for assessing developments by individuals through stages of a process. This approach is applied by using a database on ownership of 12 financial products and various demographic variables. The latent Markov model derives latent classes, representing household product portfolios, and shows the relationship between class membership and household demographics. The analysis provides insight into switching between the latent classes, reflecting developments of individual household product portfolios, and the effects of demographics on such switches. Based on this, we formulate equations to predict future acquisitions of financial products. The model accurately predicts which product a specific household unit acquires next, for most of the products.

Suggested Citation

  • Leonard J. Paas & Jeroen K. Vermunt & Tammo H. A. Bijmolt, 2007. "Discrete time, discrete state latent Markov modelling for assessing and predicting household acquisitions of financial products," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 955-974, October.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:4:p:955-974
    DOI: 10.1111/j.1467-985X.2007.00478.x
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    Cited by:

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    2. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    3. Luca De Angelis & Leonard J. Paas, 2013. "A dynamic analysis of stock markets using a hidden Markov model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1682-1700, August.
    4. Leonard Paas, 2014. "Comments on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 473-477, September.
    5. Leeflang, Peter S.H. & Bijmolt, Tammo H.A. & van Doorn, Jenny & Hanssens, Dominique M. & van Heerde, Harald J. & Verhoef, Peter C. & Wieringa, Jaap E., 2009. "Creating lift versus building the base: Current trends in marketing dynamics," International Journal of Research in Marketing, Elsevier, vol. 26(1), pages 13-20.
    6. Wouterse, Bram & Huisman, Martijn & Meijboom, Bert R. & Deeg, Dorly J.H. & Polder, Johan J., 2013. "Modeling the relationship between health and health care expenditures using a latent Markov model," Journal of Health Economics, Elsevier, vol. 32(2), pages 423-439.
    7. Marc A. Scott & Kaushik Mohan & Jacques‐Antoine Gauthier, 2020. "Model‐based clustering and analysis of life history data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1231-1251, June.
    8. De Angelis, L & Paas, L.J., 2009. "The dynamic analysis and prediction of stock markets through the latent Markov model," Serie Research Memoranda 0053, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    9. Solomon Zena Walelign & Mariève Pouliot & Helle Overgaard Larsen & Carsten Smith-Hall, 2015. "A novel approach to dynamic livelihood clustering: Empirical evidence from Nepal," IFRO Working Paper 2015/09, University of Copenhagen, Department of Food and Resource Economics.
    10. Martino, Andrea & Guatteri, Giuseppina & Paganoni, Anna Maria, 2020. "Hidden Markov Models for multivariate functional data," Statistics & Probability Letters, Elsevier, vol. 167(C).
    11. Hans Jørn Juhl & Morten H. J. Fenger & John Thøgersen, 2017. "Will the Consistent Organic Food Consumer Step Forward? An Empirical Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(3), pages 519-535.

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