A Bayesian Approach to Calibration
提出一种贝叶斯校准方法，通过设定参数先验分布来量化理论模型的不确定性，并用模拟数据与实证数据对比评估模型拟合度，以King、Plosser和Rebelo的商业周期模型为例展示该方法。
We develop a Bayesian approach to calibration that enables the incorporation of uncertainty regarding the parameters of the theoretical model under investigation. Our procedure involves the specification of prior distributions over parameter values, which in turn induce distributions over the statistical properties of artificial data simulated from the model. These distributions are compared with their empirical counterparts to assess the model's fit. The business-cycle model of King, Plosser, and Rebelo is used to demonstrate our procedure. We find that modest prior uncertainty regarding deep parameters enhances the plausibility of the model's description of the actual data.