Asymptotic properties of maximum likelihood estimator for two-step logit models

Changming Yin, Zhanfeng Wang and Hong Zhang

Statistics & Probability Letters, 2014, vol. 85, issue C, 135-143

Abstract: Two-step logit models are extensions of the ordinary logistic regression model, which are designed for complex ordinal outcomes commonly seen in practice. In this paper, we establish some asymptotic properties of the maximum likelihood estimator (MLE) of the regression parameter vector under some mild conditions, which include existence of the MLE, convergence rate and asymptotic normality of the MLE. We relax the boundedness condition of the regressors required in most existing theoretical results, and all conditions are easy to verify.

Keywords: Two-step models; Ordinal data; Rate of strong consistency; Asymptotic normality (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1016/j.spl.2013.11.014

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