arXiv 3 May 2026 · Econometrics
arXiv:2605.02085 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes an eigenvalue-based small-sample approximation of the celebrated Markov Chain Monte Carlo that delivers an invariant steady-state distribution that is consistent with traditional Monte Carlo methods. The proposed eigenvalue-based methodology reduces the number of paths required for Monte Carlo from as many as 1,000,000 to as few as 10 (depending on the simulation time horizon $T$), and delivers comparable, distributionally robust results, as measured by the Wasserstein distance. The proposed methodology also produces a significant variance reduction in the steady-state distribution.
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| Reference | Intensity | Mentions | Sections | Main text | |
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| " write$ calc.label write$ sort.year write$ " | unmatched citation key " write$ calc.label write$ sort.year write$ " | 0.405 | 1 | 1 | 100% |
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