Journal article
A Bayesian Approach to Calibration
Journal of business & economic statistics, Vol.14(1), pp.1-9
01/01/1996
DOI: 10.1080/07350015.1996.10524625
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.
Details
- Title: Subtitle
- A Bayesian Approach to Calibration
- Creators
- David N. DeJong - University of PittsburghBeth Fisher Ingram - University of IowaCharles H. Whiteman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of business & economic statistics, Vol.14(1), pp.1-9
- DOI
- 10.1080/07350015.1996.10524625
- ISSN
- 0735-0015
- eISSN
- 1537-2707
- Publisher
- Taylor & Francis Group
- Number of pages
- 9
- Language
- English
- Date published
- 01/01/1996
- Academic Unit
- Economics
- Record Identifier
- 9984962549202771
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