Advances in Structural Vector Autoregressions with Imperfect Identifying Information
Christiane Baumeister () and
James Hamilton ()
No 27014, NBER Working Papers from National Bureau of Economic Research, Inc
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.
JEL-codes: C11 C32 Q43 (search for similar items in EconPapers)
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Working Paper: Advances in Structural Vector Autoregressions with Imperfect Identifying Information (2020)
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