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Testing the Adequacy of Markov Chain and Mover-Stayer Models as Representations of Credit Behavior

Author

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  • Halina Frydman

    (New York University, New York, New York)

  • Jarl G. Kallberg

    (New York University, New York, New York)

  • Duen-Li Kao

    (New York University, New York, New York)

Abstract

We summarize methodology for testing the compatibility of discrete time stochastic processes—stationary and nonstationary Markov chains and an extension, the mover-stayer model—with longitudinal data from an unknown empirical process. We apply this methodology to determine the suitability of these models to represent the payment behavior of a sample of retail revolving credit accounts. We are led to reject stationary and also nonstationary Markov chain models for our data and for our state space definition in favor of the mover-stayer model. The mover-stayer model, in contrast to Markov chains, incorporates a simple form of population heterogeneity. Stationary Markov chains have been used extensively in finance literature to model payment behavior of credit accounts. Our empirical study suggests, however, that stationary Markov chains may not appropriately model payment behavior. It also indicates that incorporating heterogeneity in modeling payment behavior may be more important than incorporing nonstationarity.

Suggested Citation

  • Halina Frydman & Jarl G. Kallberg & Duen-Li Kao, 1985. "Testing the Adequacy of Markov Chain and Mover-Stayer Models as Representations of Credit Behavior," Operations Research, INFORMS, vol. 33(6), pages 1203-1214, December.
  • Handle: RePEc:inm:oropre:v:33:y:1985:i:6:p:1203-1214
    DOI: 10.1287/opre.33.6.1203
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    Cited by:

    1. Legrand D. F. Saint‐Cyr, 2022. "Heterogeneous farm‐size dynamics and impacts of subsidies from agricultural policy: Evidence from France," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(3), pages 893-923, September.
    2. Legrand D. F. Saint-Cyr & Laurent Piet, 2017. "Movers and stayers in the farming sector: accounting for unobserved heterogeneity in structural change," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 777-795, August.
    3. Camilla Ferretti & Giampaolo Gabbi & Piero Ganugi & Federica Sist & Pietro Vozzella, 2019. "Credit Risk Migration and Economic Cycles," Risks, MDPI, vol. 7(4), pages 1-18, October.
    4. Jonathan Crook & Tony Bellotti, 2010. "Time varying and dynamic models for default risk in consumer loans," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(2), pages 283-305, April.
    5. Saint-Cyr, Legrand D. F., 2017. "Farm heterogeneity and agricultural policy impacts on size dynamics: evidence from France," Working Papers 258013, Institut National de la recherche Agronomique (INRA), Departement Sciences Sociales, Agriculture et Alimentation, Espace et Environnement (SAE2).
    6. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    7. Dariusz Wędzki, 2007. "Trade credit portfolio selection – a markovian approach," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 17(2), pages 105-119.
    8. So, Meko M.C. & Thomas, Lyn C., 2011. "Modelling the profitability of credit cards by Markov decision processes," European Journal of Operational Research, Elsevier, vol. 212(1), pages 123-130, July.
    9. Saint-Cyr, Legrand D. F. & Piet, Laurent, 2014. "Movers and Stayers in the Farming Sector: Another Look at Heterogeneity in Structural Change," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 183068, European Association of Agricultural Economists.
    10. Sanjeev Mittal & Pankaj Gupta & K. Jain, 2011. "Neural network credit scoring model for micro enterprise financing in India," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(3), pages 224-242, October.
    11. Alexandra Schwarz, 2011. "Measurement, Monitoring, and Forecasting of Consumer Credit Default Risk - An Indicator Approach Based on Individual Payment Histories," Schumpeter Discussion Papers sdp11004, Universitätsbibliothek Wuppertal, University Library.

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