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

Arbeitspapier

The 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: CFS Working Paper Series ; No. 478

Classification and Topics

• Classification: 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
• Subject: Bayesian inference ; density forecasting ; Kalman filter ; missing data ; Monte Carlo integration ; predictive likelihood

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Warne, Anders ; Coenen, Günter ; Christoffel, Kai
• Event: Veröffentlichung
• (who): Goethe University Frankfurt, Center for Financial Studies (CFS)
• (where): Frankfurt a. M.
• (when): 2014

Further information

• Handle: http://hdl.handle.net/10419/102702
• Last update: 10.03.2025, 11:44 AM CET