Arbeitspapier
Predictive likelihood comparisons with DSGE and DSGE-VAR models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: ECB Working Paper ; No. 1536
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Model Evaluation, Validation, and Selection
Forecasting Models; Simulation Methods
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
Bayesian inference
forecasting
Kalman filter
Missing data
Monte Carlo integration
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Warne, Anders
Coenen, Günter
Christoffel, Kai
- Ereignis
-
Veröffentlichung
- (wer)
-
European Central Bank (ECB)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2013
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Warne, Anders
- Coenen, Günter
- Christoffel, Kai
- European Central Bank (ECB)
Entstanden
- 2013
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