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Sequential Monte Carlo sampling for DSGE models

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Abstract

We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models, wherein a particle approximation to the posterior is built iteratively through tempering the likelihood. Using three examples--an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohe and Uribe's (2012) news shock model--we show that the SMC algorithm is better suited for multimodal and irregular posterior distributions than the widely-used random-walk Metropolis-Hastings algorithm. We find that a more diffuse prior for the Smets and Wouters (2007) model improves its marginal data density and that a slight modification of the prior for the news shock model leads to important changes in the posterior inference about the importance of news shocks for fluctuations in hours worked. Unlike standard Markov chain Monte Carlo (MCMC) techniques, the SMC algorithm is well suited for parallel computing.

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  • Edward Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo sampling for DSGE models," Finance and Economics Discussion Series 2013-43, Board of Governors of the Federal Reserve System (U.S.), revised 2013.
  • Handle: RePEc:fip:fedgfe:2013-43
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    4. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
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    Cited by:

    1. Luc Bauwens & Jean-François Carpantier & Arnaud Dufays, 2017. "Autoregressive Moving Average Infinite Hidden Markov-Switching Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(2), pages 162-182, April.
    2. Fabio Canova & Fernando J. Pérez Forero, 2012. "Estimating Overidentified, Nonrecursive Time-Varying Coefficients Structural VARs," Working Papers 637, Barcelona Graduate School of Economics.
    3. Garland Durham & John Geweke, 2013. "Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments," Working Paper Series 9, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Isabelle Salle & Murat Yıldızoğlu, 2014. "Efficient Sampling and Meta-Modeling for Computational Economic Models," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 507-536, December.
    5. Arnaud Dufays, 2014. "On the conjugacy of off-line and on-line Sequential Monte Carlo Samplers," Working Paper Research 263, National Bank of Belgium.
    6. Marco Del Negro & Marc P. Giannoni & Frank Schorfheide, 2015. "Inflation in the Great Recession and New Keynesian Models," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 168-196, January.
    7. Hess Chung & Edward Herbst & Michael T. Kiley, 2015. "Effective Monetary Policy Strategies in New Keynesian Models: A Reexamination," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 289-344.
    8. Kiley, Michael T. & Sim, Jae, 2017. "Optimal monetary and macroprudential policies: Gains and pitfalls in a model of financial intermediation," Journal of Macroeconomics, Elsevier, vol. 54(PB), pages 232-259.
    9. Eric M. Leeper & Nora Traum & Todd B. Walker, 2017. "Clearing Up the Fiscal Multiplier Morass," American Economic Review, American Economic Association, vol. 107(8), pages 2409-2454, August.
    10. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.),Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    11. Mark Bognanni & Edward Herbst, 2014. "Estimating (Markov-Switching) VAR Models without Gibbs Sampling: A Sequential Monte Carlo Approach," Working Papers (Old Series) 1427, Federal Reserve Bank of Cleveland, revised 12 Nov 2014.
    12. Arnaud Dufays, 2016. "Evolutionary Sequential Monte Carlo Samplers for Change-Point Models," Econometrics, MDPI, Open Access Journal, vol. 4(1), pages 1-33, March.
    13. Markku Lanne & Jani Luoto, 2015. "Estimation of DSGE Models under Diffuse Priors and Data-Driven Identification Constraints," CREATES Research Papers 2015-37, Department of Economics and Business Economics, Aarhus University.
    14. Radu Titus MARINESCU & Madalina Gabriela ANGHEL & Aurelian DIACONU, 2016. "Theoretical and Practical Aspects of Analysis of Investment’s Sensitivity," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(8), pages 37-47, August.
    15. Mutschler, Willi, 2015. "Identification of DSGE models—The effect of higher-order approximation and pruning," Journal of Economic Dynamics and Control, Elsevier, vol. 56(C), pages 34-54.
    16. Kapetanios, George & Price, Simon & Theodoridis, Konstantinos, 2015. "A new approach to multi-step forecasting using dynamic stochastic general equilibrium models," Economics Letters, Elsevier, vol. 136(C), pages 237-242.
    17. Dufays, A. & Rombouts, V., 2015. "Sparse Change-Point Time Series Models," CORE Discussion Papers 2015032, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    18. Daniel F. Waggoner & Hongwei Wu & Tao Zha, 2014. "The Dynamic Striated Metropolis-Hastings Sampler for High-Dimensional Models," FRB Atlanta Working Paper 2014-21, Federal Reserve Bank of Atlanta.
    19. Edward Herbst, 2015. "Using the “Chandrasekhar Recursions” for Likelihood Evaluation of DSGE Models," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 693-705, April.

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General

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