Posterior distributions in limited information analysis of the simultaneous equations model using the Jeffreys prior
研究在联立方程模型中使用Jeffreys先验进行贝叶斯分析，推导了结构系数后验密度的精确和近似公式，发现其具有柯西型尾部，并与LIML估计量的有限样本分布相关联，同时解释了后验分布中非可积渐近尖点的成因。
This paper studies the use of the Jeffreys prior in Bayesian analysis of the simultaneous equations model (SEM). Exact representations are obtained for the posterior density of the structural coefficient β in canonical SEMs with two endogenous variables. For the general case with m endogenous variables and an unknown covariance matrix, the Laplace approximation is used to derive an analytic formula for the same posterior density. Both the exact and the approximate formulas we derive are found to exhibit Cauchy-like tails analogous to comparable results in the classical literature on LIML estimation. Moreover, in the special case of a two-equation, just-identified SEM in canonical form, the posterior density of β is shown to have the same infinite series representation as the density of the finite sample distribution of the corresponding LIML estimator. This paper also examines the occurrence, first documented in Kleibergen and van Dijk (1994a), of a nonintegrable asymptotic cusp in the posterior distribution of the coefficient matrix of the reduced-form equations for the included endogenous regressors. An explanation for this phenomenon is provided in terms of the jacobian of the mapping from the structural model to the reduced form. This interpretation assists in understanding the success of the Jeffreys prior in resolving this problem.