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Bayesian Estimation of DSGE Models$
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Edward P. Herbst and Frank Schorfheide

Print publication date: 2015

Print ISBN-13: 9780691161082

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691161082.001.0001

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Turning a DSGE Model into a Bayesian Model

Turning a DSGE Model into a Bayesian Model

(p.14) Chapter 2 Turning a DSGE Model into a Bayesian Model
Bayesian Estimation of DSGE Models

Edward P. Herbst

Frank Schorfheide

Princeton University Press

This chapter considers the turning of DSGE models into Bayesian versions by specifying a probability distribution for the innovations of the exogenous shock processes. There exists a wide variety of numerical techniques to solve DSGE models, but the chapter elaborates on a technique that involves the log-linearization of the equilibrium conditions and the solution of the resulting linear rational expectations difference equations. The approximate solution takes the form of a vector autoregressive process for the model variables, which is driven by the innovations to the exogenous shock processes, and is used as a set of state-transition equations in the state–space representation of the DSGE model. Under the assumption that these innovations are normally distributed, the log-linearized DSGE model takes the form of a linear Gaussian state–space model.

Keywords:   DSGE models, Bayesian versions, exogenous shock processes, log-linearization, equilibrium conditions, state–space model

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