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Medigap 保险市场的激励与选择效应：Dirichlet 过程混合模型应用AI 解读

This paper presents an empirical study of endogenous treatment effects in the presence of heterogeneous responses. We estimate the incentive and selection effects of having prescription drug coverage on total drug expenditures using a sample from the Medicare Current Beneficiary Survey (MCBS). A Dirichlet process mixture (DPM) model is used to model the heterogeneity in treatment effects. Rather than estimating a finite mixture model with a fixed number of components, we…

具有内生选择的有序 Probit 模型的贝叶斯分析AI 解读

This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, allowing for potential endogeneity of insurance status. The estimation is…

含内生性的两部模型贝叶斯分析：医疗支出应用AI 解读

Abstract This paper studies the effect of managed care on medical expenditure using a model in which the insurance status is assumed to be endogenous. Insurance plan choice is modeled through the multinomial probit model. The medical expenditure variable, the outcome of interest, has a significant proportion of zeros that are handled using the two‐part model, extended to handle endogenous insurance. The estimation approach is Bayesian, based on the Gibbs Sampler. The model…

存在选择的非正态处理—结果模型：设定、模拟似然估计与医疗利用应用AI 解读

We develop a specification and estimation framework for a class of nonlinear, non-normal microeconometric models of treatment and outcome with selection. A latent factor structure is used to accommodate selection into treatment and a simulated likelihood method is used for estimation. The methodology is applied to examine the causal effect of managed care, a multinomial discrete choice process, on the utilization of health care services, measured as binary indicators and…

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