Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility
We develop a sequential Monte Carlo (SMC) algorithm for Bayesian inference in vector autoregressions with stochastic volatility (VAR-SV). The algorithm builds particle approximations to the sequence of the model’s posteriors, adapting the particles from one approximation to the next as the window of available data expands. The parallelizability of the algorithm’s computations allows the adaptations to occur rapidly. Our particular algorithm exploits the ability to marginalize many parameters from the posterior analytically and embeds a known Markov chain Monte Carlo (MCMC) algorithm for the model as an effective mutation kernel for fighting particle degeneracy. We show that, relative to using MCMC alone, our algorithm increases the precision of inference while reducing computing time by an order of magnitude when estimating a medium-scale VAR-SV model.
JEL: C11, C32, C51, E17.
Keywords: Vector autoregressions, stochastic volatility, sequential Monte Carlo, particle filter, Rao-Blackwellization.
Suggested citation: Bognanni, Mark, and John Zito. 2019. “Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility.” Federal Reserve Bank of Cleveland, Working Paper no. 19-29. https://doi.org/10.26509/frbc-wp-201929.