Data-Driven Identification Constraints for DSGE Models

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http://hdl.handle.net/10138/233473

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Lanne , M & Luoto , J P 2018 , ' Data-Driven Identification Constraints for DSGE Models ' , Oxford Bulletin of Economics and Statistics , vol. 80 , no. 2 , pp. 236-258 . https://doi.org/10.1111/obes.12217

Title: Data-Driven Identification Constraints for DSGE Models
Author: Lanne, Markku; Luoto, Jani Pentti
Contributor: University of Helsinki, Department of Political and Economic Studies (2010-2017)
University of Helsinki, HECER
Date: 2018-04
Language: eng
Number of pages: 23
Belongs to series: Oxford Bulletin of Economics and Statistics
ISSN: 0305-9049
URI: http://hdl.handle.net/10138/233473
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.
Subject: 511 Economics
MONTE-CARLO METHODS
SCORING RULES
PREDICTION
SIMULATION
INFERENCE
POSTERIOR
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