具有格结构的多变量有序离散响应模型
Multivariate Ordered Discrete Response Models with Lattice Structures
AI总结：
本文研究具有格结构的多变量有序离散响应模型，在半参数和参数化框架下推导了参数、阈值及误差分布的识别条件，并通过模拟验证了估计量的性能。
摘要：
我们分析了具有格结构的多变量有序离散响应模型，对在多个维度上进行狭隘框架选择的决策者进行建模。这些模型使用功能独立的决策阈值将潜在连续过程映射为离散响应。在半参数框架下，我们将潜在过程建模为协变量索引和未观测误差之和，推导了识别参数、阈值及误差联合累积分布函数的条件。对于参数化双变量 probit 情形，我们分别推导了回归参数和阈值以及相关参数的识别，后者需要额外的协变量条件。我们概述了半参数和参数模型的估计方法，并展示了说明格模型估计量性能的模拟结果。
英文摘要：
We analyze multivariate ordered discrete response models with a lattice structure, modeling decision makers who narrowly bracket choices across multiple dimensions. These models map latent continuous processes into discrete responses using functionally independent decision thresholds. In a semiparametric framework, we model latent processes as sums of covariate indices and unobserved errors, deriving conditions for identifying parameters, thresholds, and the joint cumulative distribution function of errors. For the parametric bivariate probit case, we separately derive identification of regression parameters and thresholds, and the correlation parameter, with the latter requiring additional covariate conditions. We outline estimation approaches for semiparametric and parametric models and present simulations illustrating the performance of estimators for lattice models.
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