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Sequential Monte Carlo sampling for DSGE models

Listed author(s):
  • Edward Herbst
  • Frank Schorfheide

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-Grohe and Uribe's (2012) news shock model we show that the SMC algorithm is better suited for multi-modal and irregular posterior distributions than the widely-used random walk Metropolis-Hastings algorithm. Unlike standard Markov chain Monte Carlo (MCMC) techniques, the SMC algorithm is well suited for parallel computing.

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Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 12-27.

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Date of creation: 2012
Handle: RePEc:fip:fedpwp:12-27
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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.
  2. 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.
  3. Marco Del Negro & Frank Schorfheide, 2006. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," FRB Atlanta Working Paper 2006-16, Federal Reserve Bank of Atlanta.
  4. Nicolas Chopin, 2002. "A sequential particle filter method for static models," Biometrika, Biometrika Trust, vol. 89(3), pages 539-552, August.
  5. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Otrok, Christopher, 2001. "On measuring the welfare cost of business cycles," Journal of Monetary Economics, Elsevier, vol. 47(1), pages 61-92, February.
  11. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, Elsevier.
  12. 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.
  13. 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.
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