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Generalized Exogenous Processes in DSGE: A Bayesian Approach

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  • Alexander Meyer-Gohde
  • Daniel Neuhoff

Abstract

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.

Suggested Citation

  • Alexander Meyer-Gohde & Daniel Neuhoff, 2015. "Generalized Exogenous Processes in DSGE: A Bayesian Approach," SFB 649 Discussion Papers SFB649DP2015-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2015-014
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    Cited by:

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    3. Daniel Neuhoff, 2015. "Dynamics of Real Per Capita GDP," SFB 649 Discussion Papers SFB649DP2015-039, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Böhl, Gregor, 2021. "Efficient solution and computation of models with occasionally binding constraints," IMFS Working Paper Series 148, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).

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    More about this item

    Keywords

    Bayesian analysis; Dynamic stochastic general equilibrium model; Model evaluation; ARMA; Reversible Jump Markov Chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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