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Sequential Monte Carlo with model tempering

  • Marko Mlikota EMAIL logo and Frank Schorfheide ORCID logo EMAIL logo
Published/Copyright: May 8, 2023
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Studies in Nonlinear Dynamics & Econometrics
From the journal Studies in Nonlinear Dynamics & Econometrics Volume 28 Issue 2

Abstract

Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood evaluations, into posterior draws from the model of interest, using a sequential Monte Carlo (SMC) algorithm. We apply the technique to the estimation of a vector autoregression with stochastic volatility and two nonlinear dynamic stochastic general equilibrium models. The runtime reductions we obtain range from 27 % to 88 %.

Keywords: Bayesian computations; dynamic stochastic general equilibrium models; sequential Monte Carlo; stochastic volatility; vector autoregressions
Corresponding authors: Marko Mlikota and Frank Schorfheide, Department of Economics, University of Pennsylvania, Philadelphia, USA, E-mail: (M. Mlikota), (F. Schorfheide) (F. Schorfheide)

Funding source: National Science Foundation

Award Identifier / Grant number: SES 1851634

Acknowledgement

We thank Marco Del Negro, David Childers, and seminar and conference participants at Penn, the 2021 Conference of Swiss Economists Abroad, the 2022 Barcelona GSE Summer Forum, the 2022 Annual Conference of the IAAE in London, the 2022 ESOBE Meetings in Salzburg, and the 2022 FRB Philadelphia Conference on Frontiers in Machine Learning and Economics for helpful comments and discussions.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: Schorfheide gratefully acknowledges financial support from the National Science Foundation under Grant SES 1851634.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/snde-2022-0103).

Received: 2022-11-15
Accepted: 2023-04-09
Published Online: 2023-05-08

© 2023 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Editorial Introduction of the Special Issue of Studies in Nonlinear Dynamics and Econometrics in Honor of Herman van Dijk
  4. Review
  5. Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View
  6. Research Articles
  7. Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
  8. Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods
  9. Matrix autoregressive models: generalization and Bayesian estimation
  10. Sequential Monte Carlo with model tempering
  11. Modeling Corporate CDS Spreads Using Markov Switching Regressions
  12. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
  13. Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics
  14. Bayesian Reconciliation of Return Predictability
  15. A Dynamic Latent-Space Model for Asset Clustering
  16. Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference
  17. Bayesian Flexible Local Projections
https://doi.org/10.1515/snde-2022-0103
Keywords for this article
Bayesian computations; dynamic stochastic general equilibrium models; sequential Monte Carlo; stochastic volatility; vector autoregressions

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Editorial Introduction of the Special Issue of Studies in Nonlinear Dynamics and Econometrics in Honor of Herman van Dijk
  4. Review
  5. Challenges and Opportunities for Twenty First Century Bayesian Econometricians: A Personal View
  6. Research Articles
  7. Markov-Switching Models with Unknown Error Distributions: Identification and Inference Within the Bayesian Framework
  8. Dynamic Shrinkage Priors for Large Time-Varying Parameter Regressions Using Scalable Markov Chain Monte Carlo Methods
  9. Matrix autoregressive models: generalization and Bayesian estimation
  10. Sequential Monte Carlo with model tempering
  11. Modeling Corporate CDS Spreads Using Markov Switching Regressions
  12. Combining Large Numbers of Density Predictions with Bayesian Predictive Synthesis
  13. Bayesian inference for non-anonymous growth incidence curves using Bernstein polynomials: an application to academic wage dynamics
  14. Bayesian Reconciliation of Return Predictability
  15. A Dynamic Latent-Space Model for Asset Clustering
  16. Posterior Manifolds over Prior Parameter Regions: Beyond Pointwise Sensitivity Assessments for Posterior Statistics from MCMC Inference
  17. Bayesian Flexible Local Projections
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