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Extracting clusters from aggregate panel data: A market segmentation study

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  • Trindade, Graça
  • Dias, José G.
  • Ambrósio, Jorge

Abstract

This paper introduces a new application of the Sequential Quadratic Programing (SQP) algorithm to the context of clustering aggregate panel data. The optimization applies the SQP method in parameter estimation. The method is illustrated on synthetic and empirical data sets. Distinct models are estimated and compared with varying numbers of clusters, explanatory variables, and data aggregation.

Suggested Citation

  • Trindade, Graça & Dias, José G. & Ambrósio, Jorge, 2017. "Extracting clusters from aggregate panel data: A market segmentation study," Applied Mathematics and Computation, Elsevier, vol. 296(C), pages 277-288.
    • Handle: RePEc:eee:apmaco:v:296:y:2017:i:c:p:277-288
    DOI: 10.1016/j.amc.2016.10.012
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    References listed on IDEAS

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    1. Basalto, Nicolas & Bellotti, Roberto & De Carlo, Francesco & Facchi, Paolo & Pantaleo, Ester & Pascazio, Saverio, 2007. "Hausdorff clustering of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(2), pages 635-644.
    2. Shen, Chungen & Xue, Wenjuan & Chen, Xiongda, 2010. "Global convergence of a robust filter SQP algorithm," European Journal of Operational Research, Elsevier, vol. 206(1), pages 34-45, October.
    3. José Dias & Sofia Ramos, 2014. "The aftermath of the subprime crisis: a clustering analysis of world banking sector," Review of Quantitative Finance and Accounting, Springer, vol. 42(2), pages 293-308, February.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Elmahdy, Emad E., 2015. "A new approach for Weibull modeling for reliability life data analysis," Applied Mathematics and Computation, Elsevier, vol. 250(C), pages 708-720.
    6. De Angelis, Luca & Dias, José G., 2014. "Mining categorical sequences from data using a hybrid clustering method," European Journal of Operational Research, Elsevier, vol. 234(3), pages 720-730.
    7. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    8. Trindade, Graça & Ambrósio, Jorge, 2012. "An optimization method to estimate models with store-level data: A case study," European Journal of Operational Research, Elsevier, vol. 217(3), pages 664-672.
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    Cited by:

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    2. Xinghua Fang & Jian Zhou & Hongya Zhao & Yizeng Chen, 2022. "A biclustering-based heterogeneous customer requirement determination method from customer participation in product development," Annals of Operations Research, Springer, vol. 309(2), pages 817-835, February.

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