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Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis

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  • Leonard Paas

  • Tammo Bijmolt

  • Jeroen Vermunt

Abstract

Segmentation structures can be unstable over time. Therefore, previous research has analyzed panel data in order to provide insight into changes in segmentation structures or switches by individuals between segments. Unfortunately, panel data are often unavailable when analyzing developments across different countries and over longer time-periods. The analysis reported in this paper makes it possible to evaluate differences in segmentation structures across countries at different time-points using multiple cross sectional datasets. This provides indications into long-term cross-national developments of segmentation structures. In the utilized multilevel latent class analysis model respondents are the lower level units and data from the same country and time-point are treated as the higher level units. As an illustrative and salient empirical example we assess similarities and differences in consumer financial product portfolios across 14 EU countries from 1969 to 2003, based on three disaggregate cross-sectional databases. Copyright Sapienza Università di Roma 2015

Suggested Citation

  • Leonard Paas & Tammo Bijmolt & Jeroen Vermunt, 2015. "Long-term developments of respondent financial product portfolios in the EU: a multilevel latent class analysis," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 249-262, August.
    • Handle: RePEc:spr:metron:v:73:y:2015:i:2:p:249-262
    DOI: 10.1007/s40300-015-0067-2
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    References listed on IDEAS

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    1. Formann, Anton K., 2007. "Mixture analysis of multivariate categorical data with covariates and missing entries," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5236-5246, July.
    2. 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.
    3. Bartolucci, Francesco & Bacci, Silvia & Gnaldi, Michela, 2014. "MultiLCIRT: An R package for multidimensional latent class item response models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 971-985.
    4. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
    5. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    6. Ngwenya, Mthunzi A. & Paas, Leonard J., 2012. "Lifecycle effects on consumer financial product portfolios in South Africa: An exploratory analysis of four ethnic groups," Journal of Economic Psychology, Elsevier, vol. 33(1), pages 8-18.
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    Cited by:

    1. Marco Alfó & Francesco Bartolucci, 2015. "Latent variable models for the analysis of socio-economic data," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 151-154, August.
    2. Mustafa Raza Rabbani & M. Kabir Hassan & Austin Dejan & Abu Bashar & Md. Bokhtiar Hasan, 2024. "A bibliometric analysis of the review papers in finance: Evidence from the last two decades," Review of Financial Economics, John Wiley & Sons, vol. 42(3), pages 241-257, July.

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