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Minimum distance estimation of long-memory stochastic duration models

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  • Mauricio Zevallos

Abstract

This article proposes a minimum distance estimator for long-memory stochastic duration models, which satisfies a central limit theorem. Distinctive features of the proposed method are that it is easy to calculate and implement, allows fast estimation even for huge datasets, and provides asymptotic standard errors for the estimators. Monte Carlo experiments indicate that the proposed estimator performs very well. The proposed method is illustrated with the estimation of a real-life time series of nearly a million observations.

Suggested Citation

  • Mauricio Zevallos, 2025. "Minimum distance estimation of long-memory stochastic duration models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(19), pages 6219-6230, October.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:19:p:6219-6230
    DOI: 10.1080/03610926.2025.2450778
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