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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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PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in PRSO for personal use.date: 17 May 2022

A Crash Course in Bayesian Inference

A Crash Course in Bayesian Inference

Chapter:
(p.29) Chapter 3 A Crash Course in Bayesian Inference
Source:
Bayesian Estimation of DSGE Models
Author(s):

Edward P. Herbst

Frank Schorfheide

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691161082.003.0003

This chapter provides a self-contained review of Bayesian inference and decision making. It begins with a discussion of Bayesian inference for a simple autoregressive (AR) model, which takes the form of a Gaussian linear regression. For this model, the posterior distribution can be characterized analytically and closed-form expressions for its moments are readily available. The chapter also examines how to turn posterior distributions into point estimates, interval estimates, forecasts, and how to solve general decision problems. The chapter shows how in a Bayesian setting, the calculus of probability is used to characterize and update an individual's state of knowledge or degree of beliefs with respect to quantities such as model parameters or future observations.

Keywords:   Bayesian inference, autoregressive model, Gaussian linear regression, posterior distributions, calculus of probability

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