Modeling Stock Prices without Knowing How to Induce Stationarity
将贝叶斯方法应用于评估经济理论对动态计量模型施加的线性约束，以检验股票价格的现值模型。研究发现，在用于预测的紧先验分布下，现值模型与数据不符；但对股息与价格关系不确定的研究者而言，该模型仍可接受。
Bayesian procedures for evaluating linear restrictions imposed by economic theory on dynamic econometric models are applied to a simple class of presentvalue models of stock prices. The procedures generate inferences that are not conditional on ancillary assumptions regarding the nature of the nonstationarity that characterizes the data. Inferences are influenced by prior views concerning nonstationarity, but these views are formally incorporated into the analysis, and alternative views are easily adopted. Viewed in light of relatively tight prior distributions that have proved useful in forecasting, the present-value model seems at odds with the data. Researchers less certain of the interaction between dividends and prices would find little reason to look beyond the present-value model.