Posterior distributions in limited information analysis of the simultaneous equations model using the Jeffreys prior
Jeffreys 先验下联立方程模型有限信息分析的后验分布
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…