Gregor Kastner, Sylvia Frühwirth-Schnatter
arXiv 16 Jun 2017 · Statistics — Methodology · publishedComputational Statistics & Data Analysis (2013) · 301 citations (OpenAlex)
arXiv:1706.05280 · PDF · DOI · OpenAlex · Extracted main text
Bayesian inference for stochastic volatility models using MCMC methods highly depends on actual parameter values in terms of sampling efficiency. While draws from the posterior utilizing the standard centered parameterization break down when the volatility of volatility parameter in the latent state equation is small, non-centered versions of the model show deficiencies for highly persistent latent variable series. The novel approach of ancillarity-sufficiency interweaving has recently been shown to aid in overcoming these issues for a broad class of multilevel models. In this paper, we demonstrate how such an interweaving strategy can be applied to stochastic volatility models in order to greatly improve sampling efficiency for all parameters and throughout the entire parameter range. Moreover, this method of "combining best of different worlds" allows for inference for parameter constellations that have previously been infeasible to estimate without the need to select a particular parameterization beforehand.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Kim, S., Shephard, N. & Chib, S (1998) Stochastic volatility: Likelihood inference and comparison with ARCH models | 1.000 | 8 | 4 | 100% |
| 2 | Omori, Y., Chib, S., Shephard, N. & Nakajima, J (2007) Stochastic volatility with leverage: Fast and efficient likelihood inference | 1.000 | 5 | 3 | 100% |
| 3 | Yu, Y. & Meng, X.-L (2011) To center or not to center: That is not the question–-an ancillarity-suffiency interweaving strategy (ASIS) for boosting MCMC ef… | 0.928 | 4 | 3 | 100% |
| 4 | Strickland, C. M., Martin, G. M. & Forbes, C. S (2008) Parameterisation and efficient MCMC estimation of non-Gaussian state space models | 0.811 | 4 | 2 | 100% |
| 5 | Frühwirth-Schnatter, S (2004) Efficient Bayesian parameter estimation | 0.737 | 3 | 2 | 100% |
| 6 | Jacquier, E., Polson, N. G. & Rossi, P. E (1994) Bayesian analysis of stochastic volatility models | 0.737 | 3 | 2 | 100% |
| 7 | Pitt, M. K. & Shephard, N (1999) Analytic convergence rates and parameterization issues for the Gibbs sampler applied to state space models | 0.737 | 3 | 2 | 100% |
| 8 | Frühwirth-Schnatter, S. & Wagner, H (2010) Stochastic model specification search for Gaussian and partial non-Gaussian state space models | 0.644 | 2 | 2 | 100% |
| 9 | Liesenfeld, R. & Richard, J.-F (2006) Classical and Bayesian analysis of univariate and multivariate stochastic volatility models | 0.644 | 2 | 2 | 100% |
| 10 | McCausland, W. J., Miller, S. & Pelletier, D (2011) Simulation smoothing for state-space models: A computational efficiency analysis | 0.644 | 2 | 2 | 100% |
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