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Sequential Monte Carlo Sampling for DSGE Models

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  • Edward P. Herbst
  • Frank Schorfheide
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 consisting of an artificial state-space model, the Smets and Wouters (2007) model, and Schmitt-Grohé 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 drastic 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.

Suggested Citation

  • Edward P. Herbst & Frank Schorfheide, 2013. "Sequential Monte Carlo Sampling for DSGE Models," NBER Working Papers 19152, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19152
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    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    3. 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.
    4. 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.
    5. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, vol. 149(1), pages 2-11, April.
    6. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
    7. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    8. Strid, Ingvar & Giordani, Paolo & Kohn, Robert, 2010. "Adaptive hybrid Metropolis-Hastings samplers for DSGE models," SSE/EFI Working Paper Series in Economics and Finance 724, Stockholm School of Economics.
    9. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
    10. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    11. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    12. Otrok, Christopher, 2001. "On measuring the welfare cost of business cycles," Journal of Monetary Economics, Elsevier, vol. 47(1), pages 61-92, February.
    13. Greenwood, Jeremy & Hercowitz, Zvi & Huffman, Gregory W, 1988. "Investment, Capacity Utilization, and the Real Business Cycle," American Economic Review, American Economic Association, vol. 78(3), pages 402-417, June.
    14. Chib, Siddhartha & Ramamurthy, Srikanth, 2010. "Tailored randomized block MCMC methods with application to DSGE models," Journal of Econometrics, Elsevier, vol. 155(1), pages 19-38, March.
    15. King, Robert G. & Plosser, Charles I. & Rebelo, Sergio T., 1988. "Production, growth and business cycles : I. The basic neoclassical model," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 195-232.
    16. Rabanal, Pau & Rubio-Ramirez, Juan F., 2005. "Comparing New Keynesian models of the business cycle: A Bayesian approach," Journal of Monetary Economics, Elsevier, vol. 52(6), pages 1151-1166, September.
    17. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    18. DeJong, David N. & Ingram, Beth F. & Whiteman, Charles H., 2000. "A Bayesian approach to dynamic macroeconomics," Journal of Econometrics, Elsevier, vol. 98(2), pages 203-223, October.
    19. Strid, Ingvar, 2010. "Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2814-2835, November.
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    More about this item

    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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