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Metropolis-Hastings prefetching algorithms


  • Strid, Ingvar

    () (Dept. of Economic Statistics, Stockholm School of Economics)


Prefetching is a simple and general method for single-chain parallelisation of the Metropolis-Hastings algorithm based on the idea of evaluating the posterior in parallel and ahead of time. In this paper improved Metropolis-Hastings prefetching algorithms are presented and evaluated. It is shown how to use available information to make better predictions of the future states of the chain and increase the efficiency of prefetching considerably. The optimal acceptance rate for the prefetching random walk Metropolis-Hastings algorithm is obtained for a special case and it is shown to decrease in the number of processors employed. The performance of the algorithms is illustrated using a well-known macroeconomic model. Bayesian estimation of DSGE models, linearly or nonlinearly approximated, is identified as a potential area of application for prefetching methods. The generality of the proposed method, however, suggests that it could be applied in many other contexts as well.

Suggested Citation

  • Strid, Ingvar, 2008. "Metropolis-Hastings prefetching algorithms," SSE/EFI Working Paper Series in Economics and Finance 706, Stockholm School of Economics, revised 02 Dec 2009.
  • Handle: RePEc:hhs:hastef:0706

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    References listed on IDEAS

    1. Amisano, Gianni & Tristani, Oreste, 2010. "Euro area inflation persistence in an estimated nonlinear DSGE model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1837-1858, October.
    2. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2007. "Estimating Macroeconomic Models: A Likelihood Approach," Review of Economic Studies, Oxford University Press, vol. 74(4), pages 1059-1087.
    3. Christiano, Lawrence J. & Trabandt, Mathias & Walentin, Karl, 2011. "Introducing financial frictions and unemployment into a small open economy model," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 1999-2041.
    4. Adolfson, Malin & Laseen, Stefan & Linde, Jesper & Villani, Mattias, 2007. "Bayesian estimation of an open economy DSGE model with incomplete pass-through," Journal of International Economics, Elsevier, vol. 72(2), pages 481-511, July.
    5. Marco Del Negro & Frank Schorfheide & Frank Smets & Raf Wouters, 2004. "On the fit and forecasting performance of New Keynesian models," FRB Atlanta Working Paper 2004-37, Federal Reserve Bank of Atlanta.
    6. Ingvar Strid & Karl Walentin, 2009. "Block Kalman Filtering for Large-Scale DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 33(3), pages 277-304, April.
    7. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    8. Alessandro Cigno & Annalisa Luporini, 2018. "On the evolution of individual preferences and family rules," Working Paper series 18-07, Rimini Centre for Economic Analysis.
    9. Klein, Paul, 2000. "Using the generalized Schur form to solve a multivariate linear rational expectations model," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1405-1423, September.
    10. Ingvar Strid, 2006. "Parallel particle filters for likelihood evaluation in DSGE models: An assessment," Computing in Economics and Finance 2006 395, Society for Computational Economics.
    11. Anatoliy Belaygorod & Michael J. Dueker, 2007. "The price puzzle and indeterminacy in an estimated DSGE model," Working Papers 2006-025, Federal Reserve Bank of St. Louis.
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    More about this item


    Prefetching; Metropolis-Hastings; Parallel Computing; DSGE models; Optimal acceptance rate;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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