The authors develop a Bayesian approach to calibration which enables the incorporation of uncertainty regarding the parameters of the theoretical model under investigation. Their 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 in order to assess the model's fit. The business cycle model of R. King, C. Plosser, and S. Rebelo (1988) is used to demonstrate the authors' procedure. They find that modest prior uncertainty regarding deep parameters enhances the plausibility of the model's description of the actual data.
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