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Score-driven credit risk clustering in Guatemala: an extension for unbalanced panels

Author

Listed:
  • Szabolcs Blazsek
  • Diego Goirigolzarri
  • Ari S. Kamau

Abstract

We present the first application of a recent score-driven clustering model and extend it to unbalanced panels. We apply the new score-driven model to a random sample of customers of a large Guatemalan bank using data from April 2019 to September 2022. We use the account balance at the end of the month, the monthly total value of credits, the monthly total value of debits, the monthly number of credits, and the monthly number of debits for each customer as dependent variables. We consider alternative model specifications, present their statistical inferences, and report the estimates of the filtered probabilities of credit ratings for 402 bank customers. We compare the score-driven clustering model with the currently used multinomial logit model. The extended score-driven credit risk clustering method performs well and robustly captures the dynamics of credit ratings. This may motivate Guatemalan banks and banks in other emerging or developed countries to use the model for credit risk assessment.

Suggested Citation

  • Szabolcs Blazsek & Diego Goirigolzarri & Ari S. Kamau, 2026. "Score-driven credit risk clustering in Guatemala: an extension for unbalanced panels," Applied Economics, Taylor & Francis Journals, vol. 58(8), pages 1449-1467, February.
    • Handle: RePEc:taf:applec:v:58:y:2026:i:8:p:1449-1467
    DOI: 10.1080/00036846.2025.2467283
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