Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions
We study the convergence properties of a collapsed Gibbs sampler for Bayesian vector
autoregressions with predictors, or exogenous variables. The Markov chain generated by
our algorithm is shown to be geometrically ergodic regardless of whether the number of
observations in the underlying vector autoregression is small or large in comparison to the
order and dimension of it. In a convergence complexity analysis, we also give conditions for
when the geometric ergodicity is asymptotically stable as the number of observations tends …
autoregressions with predictors, or exogenous variables. The Markov chain generated by
our algorithm is shown to be geometrically ergodic regardless of whether the number of
observations in the underlying vector autoregression is small or large in comparison to the
order and dimension of it. In a convergence complexity analysis, we also give conditions for
when the geometric ergodicity is asymptotically stable as the number of observations tends …
Convergence Analysis of a Collapsed Gibbs Sampler for Bayesian Vector Autoregressions
K Oskar Ekvall, GL Jones - arXiv e-prints, 2019 - ui.adsabs.harvard.edu
We study the convergence properties of a collapsed Gibbs sampler for Bayesian vector
autoregressions with predictors, or exogenous variables. The Markov chain generated by
our algorithm is shown to be geometrically ergodic regardless of whether the number of
observations in the underlying vector autoregression is small or large in comparison to the
order and dimension of it. In a convergence complexity analysis, we also give conditions for
when the geometric ergodicity is asymptotically stable as the number of observations tends …
autoregressions with predictors, or exogenous variables. The Markov chain generated by
our algorithm is shown to be geometrically ergodic regardless of whether the number of
observations in the underlying vector autoregression is small or large in comparison to the
order and dimension of it. In a convergence complexity analysis, we also give conditions for
when the geometric ergodicity is asymptotically stable as the number of observations tends …
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