Andras Fulop, Jeremy Heng, Junye Li
arXiv 4 Jan 2022 · Statistics — Computation · 1 citations (OpenAlex)
arXiv:2201.01094 · PDF · DOI · OpenAlex · Extracted main text
Most solved dynamic structural macrofinance models are non-linear and/or non-Gaussian state-space models with high-dimensional and complex structures. We propose an annealed controlled sequential Monte Carlo method that delivers numerically stable and low variance estimators of the likelihood function. The method relies on an annealing procedure to gradually introduce information from observations and constructs globally optimal proposal distributions by solving associated optimal control problems that yield zero variance likelihood estimators. To perform parameter inference, we develop a new adaptive SMC$^2$ algorithm that employs likelihood estimators from annealed controlled sequential Monte Carlo. We provide a theoretical stability analysis that elucidates the advantages of our methodology and asymptotic results concerning the consistency and convergence rates of our SMC$^2$ estimators. We illustrate the strengths of our proposed methodology by estimating two popular macrofinance models: a non-linear new Keynesian dynamic stochastic general equilibrium model and a non-linear non-Gaussian consumption-based long-run risk model.
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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 | Heng, J., A. N. Bishop, G. Deligiannidis, and A. Doucet (2020) Controlled sequential Monte Carlo self | 0.961 | 9 | 3 | 89% |
| 2 | Duan, J.-C. and A. Fulop (2015) Density-tempered marginalized sequential Monte Carlo samplers | 0.941 | 6 | 4 | 83% |
| 3 | An, S. and F. Schorfheide (2007) Bayesian analysis of DSGE models | 0.874 | 9 | 2 | 100% |
| 4 | Herbst, E. and F. Schorfheide (2016) Bayesian Estimation of DSGE Models | 0.874 | 7 | 2 | 100% |
| 5 | Svensson, A., T. B. Schön, and F. Lindsten (2018) Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution | 0.843 | 3 | 3 | 100% |
| 6 | Bansal, R. and A. Yaron (2004) Risks for the long run: A potential resolution of asset pricing puzzles | 0.737 | 5 | 3 | 40% |
| 7 | Chopin, N (2004) Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference | 0.737 | 3 | 3 | 67% |
| 8 | Del Moral, P (2004) Feynman-Kac formulae | 0.737 | 3 | 3 | 67% |
| 9 | Andrieu, C., A. Doucet, and R. Holenstein (2010) Particle Markov chain Monte Carlo methods | 0.737 | 3 | 2 | 100% |
| 10 | Dai, C., J. Heng, P. E. Jacob, and N. Whiteley (2020) An invitation to sequential Monte Carlo samplers | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 65 scored citations.