Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”

Mark Bognanni

Journal of Econometrics, 2022, vol. 227, issue 2, 498-505

Abstract: Fully Bayesian inference in a vector autoregression with stochastic volatility (VAR-SV) typically relies on simulations from a multi-step Markov chain Monte Carlo (MCMC) algorithm. Carriero et al. (2019) propose a new, faster, “triangular” algorithm (TA) to replace the systemwide algorithm (SWA) in the most time-consuming step of the VAR-SV’s standard MCMC algorithm. This paper analytically shows that the TA and SWA generally sample from different distributions, thereby disproving a central claim of Carriero et al. (2019). Replacing the SWA with the TA thus results in an ad hoc change to the MCMC algorithm’s transition kernel, leaving a priori unknown the formal relationship between the model’s posterior and simulations from the MCMC algorithm using the TA.

Keywords: Markov chain Monte Carlo; Vector autoregressions; Stochastic volatility (search for similar items in EconPapers)
JEL-codes: C11 C13 C32 C53 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:2:p:498-505

DOI: 10.1016/j.jeconom.2021.10.008

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