Sampling-Based Approaches to Bayesian Inference in Econometrics

本研究的重点是贝叶斯解 计量经济学中的多重积分问题，使用一类 数值积分程序广泛称为吉布斯采样器。 该项目的目标是使贝叶斯推理， 常规和可靠的经典方法在计量经济学，并 提供解决悬而未决的计量经济学问题的办法， 迄今为止，使用贝叶斯或 经典的方法。 该项目涉及吉布斯采样器分析的扩展 发展中心极限定理适合于系统 评估数值准确度，评价对初始 条件下，并将这些方法应用于欠辨识模型。 该项目还将利用连续条件作用 吉布斯抽样的基础和相关的数据技术 增强，以发展一个模块化的处理大多数计量经济学 模型 这些模块包括半信息先验， 回归，删失回归，异方差，似乎 不相关回归，多项概率模型，减少 秩回归，潜在因素模型，多变量（移动），回归方程 平均）自回归和状态空间模型。

The focus of this research is on solutions of the Bayesian multiple integration problem in econometrics, using a class of numerical integration procedures widely known as Gibbs samplers. The objectives of the project are to make Bayesian inference as routine and reliable as classical methods in econometrics, and to provide solutions of outstanding econometric problems on which little progress has been made heretofore using either Bayesian or classical approaches. The project involves extensions of the analysis of Gibbs samplers to develop central limit theorems appropriate to the systematic assessment of numerical accuracy, evaluate senstivity to initial conditions, and apply these methods ot underidentified models. The project will also exploit the successive conditioning fundamental to Gibbs sampling and the related technique of data augmentation, to develop a modular treatment of most econometric models. These modules include semi-informative priors in regressions, censored regression, heteroskedasticity, seemingly unrelated regressions, the multinominal probit model, reduced rank regression, the latent factor model, multivariatre (moving average) autoregression, and state space models.