Rubén Loaiza-Maya, Didier Nibbering
arXiv 20 Oct 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 1 citations (OpenAlex)
arXiv:2210.11010 · PDF · DOI · OpenAlex · Extracted main text
Variational Bayes methods are a potential scalable estimation approach for state space models. However, existing methods are inaccurate or computationally infeasible for many state space models. This paper proposes a variational approximation that is accurate and fast for any model with a closed-form measurement density function and a state transition distribution within the exponential family of distributions. We show that our method can accurately and quickly estimate a multivariate Skellam stochastic volatility model with high-frequency tick-by-tick discrete price changes of four stocks, and a time-varying parameter vector autoregression with a stochastic volatility model using eight macroeconomic variables.
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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 | Huber, F., Koop, G., and Onorante, L (2021) Inducing sparsity and shrinkage in time-varying parameter models | 0.928 | 5 | 3 | 80% |
| 2 | Loaiza-Maya, R., Smith, M. S., Nott, D. J., and Danaher, P. J (2022) Fast and accurate variational inference for models with many latent variables self | 0.928 | 4 | 4 | 100% |
| 3 | Richard, J.-F. and Zhang, W (2007) Efficient high-dimensional importance sampling | 0.843 | 5 | 3 | 60% |
| 4 | Tran, M.-N., Nott, D. J., and Kohn, R (2017) Variational Bayes with intractable likelihood | 0.843 | 3 | 3 | 100% |
| 5 | Catania, L., Di Mari, R., and Santucci de Magistris, P (2022) Dynamic discrete mixtures for high-frequency prices | 0.737 | 3 | 2 | 100% |
| 6 | Koopman, S. J., Lit, R., and Lucas, A (2017) Intraday stochastic volatility in discrete price changes: the dynamic Skellam model | 0.737 | 3 | 2 | 100% |
| 7 | Quiroz, M., Nott, D. J., and Kohn, R (2022) Gaussian variational approximation for high-dimensional state space models | 0.737 | 3 | 2 | 100% |
| 8 | Ong, V. M.-H., Nott, D. J., and Smith, M. S (2018) Gaussian variational approximation with a factor covariance structure | 0.644 | 3 | 2 | 67% |
| 9 | Carriero, A., Clark, T. E., and Marcellino, M (2019) Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.644 | 2 | 2 | 100% |
| 10 | Frazier, D. T., Loaiza-Maya, R., and Martin, G. M (2022) Variational Bayes in state space models: Inferential and predictive accuracy self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 42 scored citations.