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Open AccessArticle

Smoothed Maximum Score Estimation of Discrete Duration Models

1
Nanyang Business School, Nanyang Technological University, Singapore 639798, Singapore
2
Department of Economics, York University, Toronto, ON M3J 1P3, Canada
*
Author to whom correspondence should be addressed.
J. Risk Financial Manag. 2019, 12(2), 64; https://doi.org/10.3390/jrfm12020064
Received: 7 March 2019 / Revised: 8 April 2019 / Accepted: 9 April 2019 / Published: 15 April 2019
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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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. View Full-Text
Keywords: maximum score estimator; discrete duration models; efficient semiparamteric estimation maximum score estimator; discrete duration models; efficient semiparamteric estimation
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Reza, S.; Rilstone, P. Smoothed Maximum Score Estimation of Discrete Duration Models. J. Risk Financial Manag. 2019, 12, 64.

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