Bayesian Inference for Heteroskedastic Proxy-SVARs

Andrea Carriero, Todd Clark, Massimiliano Marcellino and Tommaso Tornese

No 22022, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We develop a Bayesian posterior simulation method for SVAR models characterized by conditionally heteroskedastic shocks and nontriangular zero restrictions, a class that includes Proxy-SVARs as a special case. The method embeds a conditional Sequential Monte Carlo (SMC) step within a Gibbs sampler. The SMC step exploits an auxiliary homoskedastic model to construct a proposal density and progressively adapts the corresponding particle distribution to incorporate heteroskedasticity, moving through a sequence of intermediate densities. In Monte Carlo experiments, the sampler recovers the structural parameters and impulse responses and produces narrower intervals than the homoskedastic model. Applications to technology and personal income tax shocks show that allowing for stochastic volatility can reduce posterior uncertainty and alter estimated responses.

Keywords: External instrument; Heteroskedasticity (search for similar items in EconPapers)
JEL-codes: C11 C32 E32 (search for similar items in EconPapers)
Date: 2026-10
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