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Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis

Published online by Cambridge University Press:  04 January 2017

John T. Williams
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Extract

The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

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Type
Research Article
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Political Analysis , Volume 4 , 1992, pp. 97 - 125
Copyright
Copyright © by the University of Michigan 1993 

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