Generalized exogenous processes in DSGE: A Bayesian approach

Arbeitspapier

The Reversible Jump Markov Chain Monte Carlo (RJMCMC) method can enhance Bayesian DSGE estimation by sampling from a posterior distribution spanning potentially nonnested models with parameter spaces of different dimensionality. We use the method to jointly sample from an ARMA process of unknown order along with the associated parameters. We apply the method to the technology process in a canonical neoclassical growth model using post war US GDP data and find that the posterior decisively rejects the standard AR(1) assumption in favor of higher order processes. While the posterior contains significant uncertainty regarding the exact order, it concentrates posterior density on hump-shaped impulse responses. A negative response of hours to a positive technology shock is within the posterior credible set when noninvertible MA representations are admitted.

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• Language: Englisch

References and Relationships

• Bibliographic citation: Series: SFB 649 Discussion Paper ; No. 2015-014

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 Construction and Estimation ; Model Evaluation, Validation, and Selection
• Subject: Bayesian analysis ; Dynamic stochastic general equilibrium model ; Model evaluation ; ARMA ; Reversible Jump Markov Chain Monte Carlo

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Meyer-Gohde, Alexander ; Neuhoff, Daniel
• Event: Veröffentlichung
• (who): Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
• (where): Berlin
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/119420