Composite Likelihood Estimation of an Autoregressive Panel Ordered Probit Model with Random Effects

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Kerem Tuzcuoglu
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引用次数: 3

Abstract

Abstract Modeling and estimating autocorrelated discrete data can be challenging. In this article, we use an autoregressive panel ordered probit model where the serial correlation in the discrete variable is driven by the autocorrelation in the latent variable. In such a nonlinear model, the presence of a lagged latent variable results in an intractable likelihood containing high-dimensional integrals. To tackle this problem, we use composite likelihoods that involve a much lower order of integration. However, parameter identification might potentially become problematic since the information employed in lower dimensional distributions may not be rich enough for identification. Therefore, we characterize types of composite likelihoods that are valid for this model and study conditions under which the parameters can be identified. Moreover, we provide consistency and asymptotic normality results for two different composite likelihood estimators and conduct Monte Carlo studies to assess their finite-sample performances. Finally, we apply our method to analyze corporate bond ratings. Supplementary materials for this article are available online.
具有随机效应的自回归面板有序概率模型的复合似然估计
摘要建模和估计自相关离散数据可能具有挑战性。在本文中,我们使用了一个自回归面板有序probit模型,其中离散变量中的序列相关性由潜在变量中的自相关驱动。在这样的非线性模型中,滞后潜变量的存在导致了包含高维积分的难以处理的似然性。为了解决这个问题,我们使用了包含低阶积分的复合可能性。然而,参数识别可能会成为潜在的问题,因为在低维分布中使用的信息可能不够丰富,无法进行识别。因此,我们描述了对该模型有效的复合可能性的类型,并研究了可以识别参数的条件。此外,我们提供了两种不同的复合似然估计的一致性和渐近正态性结果,并进行了蒙特卡罗研究来评估它们的有限样本性能。最后,我们将我们的方法应用于分析公司债券评级。本文的补充材料可在线获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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