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Bayesian Inference for Heteroskedastic Proxy-SVARs

Author

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  • Carriero, Andrea
  • Clark, Todd
  • Marcellino, Massimiliano
  • Tornese, Tommaso

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.

Suggested Citation

  • Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano & Tornese, Tommaso, 2026. "Bayesian Inference for Heteroskedastic Proxy-SVARs," CEPR Discussion Papers 22022, Centre for Economic Policy Research.
    • Handle: RePEc:cpr:ceprdp:22022
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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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