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  • 标题:Predictive likelihood comparisons with DSGE and DSGE-VAR models
  • 本地全文:下载
  • 作者:Anders Warne ; Günter Coenen ; Kai Christoffel
  • 期刊名称:Euro Area Balance of Payments and International Investment Position Statistics
  • 印刷版ISSN:1830-3420
  • 电子版ISSN:1830-3439
  • 出版年度:2013
  • 出版社:European Central Bank
  • 摘要:This paper shows how to compute the h-step-ahead predictive likelihood for any subset of the observed variables in parametric discrete time series models estimated with Bayesian methods. The subset of variables may vary across forecast horizons and the problem thereby covers marginal and joint predictive likelihoods for a fixed subset as special cases. The basic idea is to utilize well-known techniques for handling missing data when computing the likelihood function, such as a missing observations consistent Kalman filter for linear Gaussian models, but it also extends to nonlinear, nonnormal state-space models. The predictive likelihood can thereafter be calculated via Monte Carlo integration using draws from the posterior distribution. As an empirical illustration, we use euro area data and compare the forecasting performance of the New Area-Wide Model, a small-open-economy DSGE model, to DSGEVARs, and to reduced-form linear Gaussian models.
  • 关键词:Bayesian inference; forecasting; Kalman filter; missing data; Monte Carlo integration.
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