Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments
采用贝叶斯方法处理内生性模型，允许工具变量不完美，并提出一种半解析方法计算参数边际后验分布，以解决部分识别导致的估计不精确问题，并应用于体重指数对收入影响的研究。
In this paper we, like several studies in the recent literature, employ a Bayesian approach to estimation and inference in models with endogeneity concerns by imposing weaker prior assumptions than complete excludability. When allowing for instrument imperfection of this type, the model is only partially identified, and as a consequence standard estimates obtained from the Gibbs simulations can be unacceptably imprecise. We thus describe a substantially improved 'semi-analytic' method for calculating parameter marginal posteriors of interest that only require use of the well-mixing simulations associated with the identifiable model parameters and the form of the conditional prior. Our methods are also applied in an illustrative application involving the impact of body mass index on earnings.