The Quantitative Significance of the Lucas Critique
检验卢卡斯批判在贝叶斯向量自回归宏观模型中的定量重要性，发现政策规则变化会导致系数不稳定并降低条件预测方法的有效性。
Doan, Litterman, and Sims (DLS) have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression (BVAR) models. Their method seems to violate the Lucas critique, which implies that coefficients of a BVAR model will change when there is a change in policy rules. In this article, we attempt to determine whether the Lucas critique is important quantitatively in a BVAR macro model that we construct. We find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the DLS method.