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Bayesian Inference in Dynamic Econometric Models$
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Luc Bauwens, Michel Lubrano, and Jean-François Richard

Print publication date: 2000

Print ISBN-13: 9780198773122

Published to Oxford Scholarship Online: September 2011

DOI: 10.1093/acprof:oso/9780198773122.001.0001

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Dynamic Regression Models

Dynamic Regression Models

Chapter:
(p.129) 5 Dynamic Regression Models
Source:
Bayesian Inference in Dynamic Econometric Models
Author(s):

Luc Bauwens

Michel Lubrano

Jean-François Richard

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198773122.003.0005

This chapter examines the application of the dynamic regression models for inference and prediction with dynamic econometric models. It shows how to extend to the dynamic case the notion of Bayesian cut seen in the static case to justify conditional inference. The chapter also explains how Bayesian inference can be used for single-equation dynamic models. It discusses the particular case of models with autoregressive errors, discusses the issues of moving average errors, and illustrates the empirical use of the error correction model by an analysis of a money demand function for Belgium.

Keywords:   dynamic regression models, econometric models, Bayesian inference, single-equation models, autoregressive errors, moving average errors, error correction, money demand

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