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

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 codes: C11, C15, C32, C52, E3, E4, E5.

Suggested citation: Bognanni, Mark. 2018. ”A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification.“ Federal Reserve Bank of Cleveland, Working Paper no. 18-11.

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