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Smoothed Maximum Score Estimation of Discrete Duration Models

Author

Listed:
  • Sadat Reza

    (Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore)

  • Paul Rilstone

    (Department of Economics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

This paper extends Horowitz’s smoothed maximum score estimator to discrete-time duration models. The estimator’s consistency and asymptotic distribution are derived. Monte Carlo simulations using various data generating processes with varying error distributions and shapes of the hazard rate are conducted to examine the finite sample properties of the estimator. The bias-corrected estimator performs reasonably well for the models considered with moderately-sized samples.

Suggested Citation

  • Sadat Reza & Paul Rilstone, 2019. "Smoothed Maximum Score Estimation of Discrete Duration Models," JRFM, MDPI, vol. 12(2), pages 1-16, April.
    • Handle: RePEc:gam:jjrfmx:v:12:y:2019:i:2:p:64-:d:222990
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    References listed on IDEAS

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    Cited by:

    1. Thanasis Stengos, 2019. "Nonparametric Econometric Methods and Applications," JRFM, MDPI, vol. 12(4), pages 1-3, November.

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