Generalized exogenous processes in DSGE: a Bayesian approach

  • The authors relax the standard assumption in the dynamic stochastic general equilibrium (DSGE) literature that exogenous processes are governed by AR(1) processes and estimate ARMA (p,q) orders and parameters of exogenous processes. Methodologically, they contribute to the Bayesian DSGE literature by using Reversible Jump Markov Chain Monte Carlo (RJMCMC) to sample from the unknown ARMA orders and their associated parameter spaces of varying dimensions. In estimating the technology process in the neoclassical growth model using post war US GDP data, they cast considerable doubt on the standard AR(1) assumption in favor of higher order processes. They find that the posterior concentrates density on hump-shaped impulse responses for all endogenous variables, consistent with alternative empirical estimates and the rigidities behind many richer structural models. Sampling from noninvertible MA representations, a negative response of hours to a positive technology shock is contained within the posterior credible set. While the posterior contains significant uncertainty regarding the exact order, the results are insensitive to the choice of data filter; this contrasts with the authors’ ARMA estimates of GDP itself, which vary significantly depending on the choice of HP or first difference filter.

Download full text files

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Alexander Meyer-GohdeORCiD, Daniel Neuhoff
URN:urn:nbn:de:hebis:30:3-475999
URL:https://www.imfs-frankfurt.de/de/forschung/imfs-working-papers/
Parent Title (English):Working paper series / Institute for Monetary and Financial Stability ; 125
Series (Serial Number):Working paper series / Institute for Monetary and Financial Stability (125)
Publisher:Johann Wolfgang Goethe-Univ., Inst. for Monetary and Financial Stability
Place of publication:Frankfurt am Main
Document Type:Working Paper
Language:English
Year of Completion:2018
Year of first Publication:2018
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/09/27
Tag:ARMA; Bayesian analysis; Dynamic stochastic general equilibrium model; Model evaluation; Reversible Jump Markov Chain Monte Carlo
Issue:This Version: September 24, 2018
Page Number:42
HeBIS-PPN:439413311
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Institute for Monetary and Financial Stability (IMFS)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C11 Bayesian Analysis
C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models / C32 Time-Series Models; Dynamic Quantile Regressions (Updated!)
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C51 Model Construction and Estimation
C Mathematical and Quantitative Methods / C5 Econometric Modeling / C52 Model Evaluation and Selection
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht