A Bayesian Approach to Calibration

David N. DeJong · Beth Fisher Ingram · Charles H. Whiteman

111 citations0/0 files storedJournal of Business and Economic Statistics

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Monetary Policy and Economic Impact Statistical Methods and Bayesian Inference Economic theories and models

Abstract

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.

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References

1988Production, growth and business cycles 1986Investment, Capacity Utilization and the Real Business Cycle 1986Theory ahead of business-cycle measurement 1990Current Real Business Cycle Theories and Aggregate Labor Market Fluctuations 2010International Economic Review 1991Homework in Macroeconomics: Household Production and Aggregate Fluctuations 1993Estimating nonlinear time-series models using simulated vector autoregressions 1991The Allocation of Capital and Time over the Business Cycle

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