Working Paper

A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification

Abstract: This paper develops a new class of structural vector autoregressions (SVARs) with time-varying parameters, which I call a drifting SVAR (DSVAR). The DSVAR is the first structural time-varying parameter model to allow for internally consistent probabilistic inference under exact?or set?identification, nesting the widely used SVAR framework as a special case. I prove that the DSVAR implies a reduced-form representation, from which structural inference can proceed similarly to the widely used two-step approach for SVARs: beginning with estimation of a reduced form and then choosing among observationally equivalent candidate structural parameters via the imposition of identifying restrictions. In a special case, the implied reduced form is a tractable known model for which I provide the first algorithm for Bayesian estimation of all free parameters. I demonstrate the framework in the context of Baumeister and Peersman?s (2013b) work on time variation in the elasticity of oil demand.

Keywords: structural vector autoregressions; time-varying parameters; Gibbs sampling; stochastic volatility; Bayesian inference; ;

JEL Classification: C11; C15; C32; C52; E3; E4; E5;

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Bibliographic Information

Provider: Federal Reserve Bank of Cleveland

Part of Series: Working Papers (Old Series)

Publication Date: 2018-09-11

Number: 1811

Pages: 61 pages