The Systematic Specification of a Full Prior Covariance Matrix for Asset Demand Equations
提出一种分层方法，将先验不确定性的有限个不同原因转化为完整的先验协方差矩阵，用于资产需求方程的贝叶斯估计，解决线性支出系统估计复杂的问题。
Linear expenditure systems are widely used to describe consumption and portfolio decisions. However, the complexity of these models makes estimation a formidable task. In earlier work, an exchangeability assumption was used to incorporate subjective a priori information into the estimation of asset demand equations. Here, an alternative hierarchical approach is described and illustrated. This procedure provides a framework in which the identification of a limited number of distinct reasons for prior uncertainty can be converted into a full prior covariance matrix. Such a matrix can then be combined with prior means and the sample data to yield Bayesian parameter estimates.