Marginalized predictive likelihood comparisons of linear Gaussian state-space models with applications to DSGE, DSGE-VAR, and VAR models

  • he predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models with Bayesian methods, and proposes to utilize a missing observations consistent Kalman filter in the process of achieving this objective. As an empirical application, we analyze euro area data and compare the density forecast performance of a DSGE model to DSGE-VARs and reduced-form linear Gaussian models.

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Metadaten
Author:Anders Warne, Günter Coenen, Kai Christoffel
URN:urn:nbn:de:hebis:30:3-351109
URL:http://ssrn.com/abstract=2507827
DOI:https://doi.org/10.2139/ssrn.2507827
Parent Title (English):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 478
Series (Serial Number):CFS working paper series (478)
Publisher:Center for Financial Studies
Place of publication:Frankfurt, M.
Document Type:Working Paper
Language:English
Year of Completion:2014
Year of first Publication:2014
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2014/10/20
Tag:Bayesian inference; Kalman filter; Monte Carlo integration; density forecasting; missing data; predictive likelihood
Issue:June 27, 2014
Page Number:27
HeBIS-PPN:351157271
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C11 Bayesian Analysis
C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models / C32 Time-Series Models; Dynamic Quantile Regressions (Updated!)
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C52 Model Evaluation and Selection
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C53 Forecasting and Other Model Applications
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht