Citation

Discussion Paper Details

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Title: Advances in Structural Vector Autoregressions with Imperfect Identifying Information

Author(s): Christiane Baumeister and James Hamilton

Publication Date: April 2020

Keyword(s): Bayesian Analysis, Elasticities, identification, proxy VARs, sign restrictions and structural vector autoregressions

Programme Area(s): International Macroeconomics and Finance and Monetary Economics and Fluctuations

Abstract: This paper examines methods for structural interpretation of vector autoregressions when the identifying information is regarded as imperfect or incomplete. We suggest that a Bayesian approach offers a unifying theme for guiding inference in such settings. Among other advantages, the unified approach solves a problem with calculating elasticities that appears not to have been recognized by earlier researchers. We also call attention to some computational concerns of which researchers who approach this problem using other methods should be aware.

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Bibliographic Reference

Baumeister, C and Hamilton, J. 2020. 'Advances in Structural Vector Autoregressions with Imperfect Identifying Information'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=14603