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离散数据联立方程模型的似然设定AI 解读

In this article, we examine the foundations of a rich and diverse literature in economics and derive the likelihood function of simultaneous equation models for discrete data as the invariant distribution of a suitably specified Markov process. This formulation offers a well-defined reduced form of the model and dispenses with the need for controversial recursivity requirements and ad hoc indeterminacy rules. The derivation resolves puzzling paradoxes highlighted in earlier…

二元纵向数据半参数动态模型的推断AI 解读

This article deals with the analysis of a hierarchical semiparametric model for dynamic binary longitudinal responses. The main complicating components of the model are an unknown covariate function and serial correlation in the errors. Existing estimation methods for models with these features are of O(N3), where N is the total number of observations in the sample. Therefore, nonparametric estimation is largely infeasible when the sample size is large, as in typical in the…

利用 Metropolis–Hastings 输出估计边际似然AI 解读

This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons. The approach extends and completes the method presented in Chib (1995) by overcoming the problems associated with the presence of intractable full conditional densities. The proposed method is developed in the context of MCMC chains produced by the Metropolis–Hastings algorithm, whose building blocks are used both for sampling and marginal likelihood…