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Metropolis-Hastings prefetching algorithms

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  • Strid, Ingvar

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

Abstract

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.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 706.

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Length: 39 pages
Date of creation: 02 Dec 2008
Date of revision: 02 Dec 2009
Publication status: Published as Strid, Ingvar, 'Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach' in Computational Statistics and Data Analysis, 2010, pages 2814-2835.
Handle: RePEc:hhs:hastef:0706

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

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  1. Christiano, Lawrence J. & Trabandt, Mathias & Walentin, Karl, 2007. "Introducing Financial Frictions and Unemployment into a Small Open Economy Model," Working Paper Series 214, Sveriges Riksbank (Central Bank of Sweden), revised 01 Jun 2011.
  2. Schmitt-Grohé, Stephanie & Uribe, Martín, 2001. "Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function," CEPR Discussion Papers 2963, C.E.P.R. Discussion Papers.
  3. Ingvar Strid & Karl Walentin, 2009. "Block Kalman Filtering for Large-Scale DSGE Models," Computational Economics, Society for Computational Economics, vol. 33(3), pages 277-304, April.
  4. Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2006. "Estimating Macroeconomic Models: A Likelihood Approach," Levine's Bibliography 122247000000000849, UCLA Department of Economics.
  5. 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.
  6. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2005. "On the Fit and Forecasting Performance of New Keynesian Models," CEPR Discussion Papers 4848, C.E.P.R. Discussion Papers.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
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