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Federal Reserve Bank of Cleveland
Working Papers (Old Series)
A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification
Mark Bognanni

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.

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Mark Bognanni, A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification, Federal Reserve Bank of Cleveland, Working Papers (Old Series) 1811, 11 Sep 2018.
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Keywords: structural vector autoregressions; time-varying parameters; Gibbs sampling; stochastic volatility; Bayesian inference
DOI: 10.26509/frbc-wp-201811
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