arXiv 17 Nov 2025 · Econometrics
arXiv:2511.12847 · PDF · DOI · OpenAlex · Extracted main text
Leaving posterior sensitivity concerns aside, non-identifiability of the parameters does not raise a difficulty for Bayesian inference as far as the posterior is proper, but multi-modality or flat regions of the posterior induced by the lack of identification leaves a challenge for modern Bayesian computation. Sampling methods often struggle with slow or non-convergence when dealing with multiple modes or flat regions of the target distributions. This paper develops a novel Markov chain Monte Carlo (MCMC) approach for non-identified models, leveraging the knowledge of observationally equivalent sets of parameters, and highlights an important role that identification plays in modern Bayesian analysis. We show that our identification-aware proposal eliminates mode entrapment, achieving a convergence rate uniformly bounded away from zero, in sharp contrast to the exponentially decaying rates characterizing standard Random Walk Metropolis and Hamiltonian Monte Carlo. Simulation studies show its superior performance compared to other popular computational methods including Hamiltonian Monte Carlo and sequential Monte Carlo. We also demonstrate that our method uncovers non-trivial modes in the target distribution in a structural vector moving-average (SVMA) application.
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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 | Herbst, Edward and Schorfheide, Frank (2014) Sequential Monte Carlo sampling for DSGE models | 0.941 | 6 | 3 | 83% |
| 2 | Plagborg-Møller, Mikkel (2019) Bayesian inference on structural impulse response functions | 0.928 | 10 | 3 | 80% |
| 3 | Lippi, Marco and Reichlin, Lucrezia (1994) VAR analysis, nonfundamental representations, Blaschke matrices | 0.737 | 3 | 3 | 67% |
| 4 | Roberts, Gareth O and Rosenthal, Jeffrey S (2004) General state space Markov chains and MCMC algorithms | 0.737 | 3 | 3 | 67% |
| 5 | Geweke, John (2007) Interpretation and inference in mixture models: Simple MCMC works | 0.737 | 3 | 2 | 100% |
| 6 | Meyn, Sean P and Tweedie, Richard L (2009) Markov chains and stochastic stability | 0.644 | 3 | 2 | 67% |
| 7 | Bacchiocchi, Emanuele and Kitagawa, Toru (2025) Locally-but not globally-identified SVARs self | 0.644 | 2 | 2 | 100% |
| 8 | Frühwirth-Schnatter, Sylvia (2006) Finite mixture and Markov switching models | 0.644 | 2 | 2 | 100% |
| 9 | Giacomini, Raffaella and Kitagawa, Toru (2021) Robust Bayesian inference for set-identified models self | 0.644 | 2 | 2 | 100% |
| 10 | Guan, Yongtao and Krone, Stephen M (2007) Small-world MCMC and convergence to multi-modal distributions: From slow mixing to fast mixing | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.