Data-Driven Identification Constraints for DSGE Models

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Abstract

We propose imposing data-driven identification constraints to alleviate the multimodality problem arising in the estimation of poorly identified dynamic stochastic general equilibrium models under non-informative prior distributions. We also devise an iterative procedure based on the posterior density of the parameters for finding these constraints. An empirical application to the Smets and Wouters () model demonstrates the properties of the estimation method, and shows how the problem of multimodal posterior distributions caused by parameter redundancy is eliminated by identification constraints. Out-of-sample forecast comparisons as well as Bayes factors lend support to the constrained model.

Original languageEnglish
JournalOxford Bulletin of Economics and Statistics
Volume80
Issue number2
Pages (from-to)236-258
Number of pages23
ISSN0305-9049
DOIs
Publication statusPublished - Apr 2018
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 511 Economics
  • MONTE-CARLO METHODS
  • SCORING RULES
  • PREDICTION
  • SIMULATION
  • INFERENCE
  • POSTERIOR

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