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

Titel: Data-Driven Identification Constraints for DSGE Models
Författare: Lanne, Markku; Luoto, Jani Pentti
Upphovmannens organisation: Department of Political and Economic Studies (2010-2017)
Economics
Helsinki Center of Economic Research (HECER)
Financial and Macroeconometrics
HECER
Datum: 2018-04
Språk: eng
Sidantal: 23
Tillhör serie: Oxford Bulletin of Economics and Statistics
ISSN: 0305-9049
DOI: https://doi.org/10.1111/obes.12217
Permanenta länken (URI): http://hdl.handle.net/10138/233473
Abstrakt: 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
Referentgranskad: Ja
Licens: cc_by
Användningsbegränsning: openAccess
Parallelpublicerad version: publishedVersion


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