Working Paper: CEPR ID: DP14603
Authors: Christiane Baumeister; James Hamilton
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.
Keywords: Structural Vector Autoregressions; Bayesian Analysis; Identification; Elasticities; Sign Restrictions; Proxy Vars
JEL Codes: C11; C32; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Traditional identifying assumptions (C50) | Incorrect conclusions (Y50) |
| Bayesian approach (C11) | Accurate representation of uncertainty regarding economic structure (D89) |
| Bayesian approach (C11) | More reliable estimates of structural parameters (C51) |
| Previous methods for calculating elasticities in sign-restricted VARs (C51) | Flawed estimates (C51) |
| Bayesian approach (C11) | Rectifies issues in calculating elasticities (C51) |
| Sign restrictions alone (C29) | Overly large identified set (C55) |
| Bayesian interpretation of identifying assumptions (C11) | More flexible approach (B50) |
| Bayesian approach (C11) | Better-informed policy implications (D78) |
| Computational efficiency of Bayesian approach (C11) | Avoid arbitrary restrictions (P14) |
| Thoughtful consideration of prior information (D83) | More robust economic insights (E39) |