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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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From Linear to Nonlinear DSGE Models

From Linear to Nonlinear DSGE Models

(p.163) Chapter 7 From Linear to Nonlinear DSGE Models
Bayesian Estimation of DSGE Models

Edward P. Herbst

Frank Schorfheide

Princeton University Press

This chapter presents computational techniques that can be used to estimate DSGE models that have been solved with nonlinear techniques, such as higher-order perturbation methods or projection methods. From the perspective of Bayesian estimation, the key difference between DSGE models that have been solved with a linearization technique and models that have been solved nonlinearly is that in the former case, the resulting state–space representation is linear, whereas in the latter case, it takes the general nonlinear form. The chapter also highlights some of the features that researchers have introduced into DSGE models to capture important nonlinearities in the data, wherein it uses the small-scale New Keynesian DSGE model as illustrative example.

Keywords:   DSGE models, nonlinear techniques, Bayesian estimation, state–space representation, New Keynesian DSGE model, linearization techniques

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