A bayesian approach to calibration

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

Research output: Contribution to journalArticle

41 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1-9
Number of pages9
JournalJournal of Business and Economic Statistics
Volume14
Issue number1
DOIs
StatePublished - Jan 1996

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Bayesian Approach
Calibration
Uncertainty
Business Cycles
uncertainty
Prior distribution
Model
Theoretical Model
Statistical property
business cycle
Specification
Bayesian approach
Demonstrate

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this

DeJong, David N. ; Ingram, Beth Fisher ; Whiteman, Charles H. / A bayesian approach to calibration. In: Journal of Business and Economic Statistics. 1996 ; Vol. 14, No. 1. pp. 1-9.
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A bayesian approach to calibration. / DeJong, David N.; Ingram, Beth Fisher; Whiteman, Charles H.

In: Journal of Business and Economic Statistics, Vol. 14, No. 1, 01.1996, p. 1-9.

Research output: Contribution to journalArticle

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