Mike Tsionas, Marwan Izzeldin, Lorenzo Trapani
arXiv 28 Dec 2019 · Econometrics · publishedEuropean Economic Review (2021) · 9 citations (OpenAlex)
arXiv:1912.12527 · PDF · DOI · OpenAlex · Extracted main text
This paper provides a simple, yet reliable, alternative to the (Bayesian) estimation of large multivariate VARs with time variation in the conditional mean equations and/or in the covariance structure. With our new methodology, the original multivariate, n dimensional model is treated as a set of n univariate estimation problems, and cross-dependence is handled through the use of a copula. Thus, only univariate distribution functions are needed when estimating the individual equations, which are often available in closed form, and easy to handle with MCMC (or other techniques). Estimation is carried out in parallel for the individual equations. Thereafter, the individual posteriors are combined with the copula, so obtaining a joint posterior which can be easily resampled. We illustrate our approach by applying it to a large time-varying parameter VAR with 25 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 | Guhaniyogi, R. and D. B. Dunson (2015) Bayesian compressed regression | 1.000 | 6 | 3 | 100% |
| 2 | Koop, G. and D. Korobilis (2013) Large time-varying parameter VARs | 0.937 | 17 | 4 | 82% |
| 3 | Bańbura, M., D. Giannone, and L. Reichlin (2010) Large Bayesian vector auto regressions | 0.874 | 6 | 2 | 100% |
| 4 | Creal, D. D. and R. S. Tsay (2015) High dimensional dynamic stochastic copula models | 0.737 | 3 | 2 | 100% |
| 5 | Giannone, D., M. Lenza, and G. E. Primiceri (2015) Prior selection for vector autoregressions | 0.737 | 3 | 2 | 100% |
| 6 | Uhlig, H (1997) Bayesian vector autoregressions with stochastic volatility | 0.737 | 3 | 2 | 100% |
| 7 | Carriero, A., T. E. Clark, and M. Marcellino (2019) Large bayesian vector autoregressions with stochastic volatility and non-conjugate priors | 0.693 | 6 | 1 | 100% |
| 8 | Nemeth, C., C. Sherlock, and P. Fearnhead (2016) Particle Metropolis-adjusted Langevin algorithms | 0.644 | 4 | 1 | 100% |
| 9 | Chan, J. C., E. Eisenstat, et al (2013) Gibbs samplers for VARMA and its extensions | 0.644 | 2 | 2 | 100% |
| 10 | Kim, S., N. Shephard, and S. Chib (1998) Stochastic volatility: likelihood inference and comparison with ARCH models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 63 scored citations.