SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS

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 two empirical illustrations consisting of the Smets and Wou...

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Veröffentlicht in:	Journal of applied econometrics (Chichester, England) Jg. 29; H. 7; S. 1073 - 1098
Hauptverfasser:	Herbst, Edward, Schorfheide, Frank
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Chichester Blackwell Publishing Ltd 01.11.2014 Wiley (Variant) Wiley-Blackwell Wiley Periodicals Inc
Schlagworte:	Algorithms Bayesian analysis Bayesian method Dynamisches Gleichgewicht Economic models Economic shock Function words General economic equilibrium Inference Markov analysis Markovian processes Monte Carlo simulation Random walk theory Sampling Schätztheorie Stichprobenerhebung Stochastic models Stochastic processes Studies Theorie Working hours
ISSN:	0883-7252, 1099-1255
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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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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.
AbstractList	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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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. 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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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. Copyright © 2014 John Wiley & Sons, Ltd. 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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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. Copyright © 2014 John Wiley & Sons, Ltd. Copyright John Wiley & Sons. Reproduced with Permission. An electronic version of this article is available online at http://www.interscience.wiley.com SUMMARYWe 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 two empirical illustrations consisting of the Smets and Wouters model and a larger 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 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. Copyright © 2014 John Wiley & Sons, Ltd.
Author	Herbst, Edward Schorfheide, Frank
Author_xml	– sequence: 1 givenname: Edward surname: Herbst fullname: Herbst, Edward email: edward.p.herbst@frb.gov organization: Board of Governors of the Federal Reserve System, DC, Washington, USA – sequence: 2 givenname: Frank surname: Schorfheide fullname: Schorfheide, Frank organization: Department of Economics, University of Pennsylvania, Philadelphia, PA, USA
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References_xml	– reference: Greenwood J, Hercowitz Z, Huffman G. 1988. Investment, capacity utilization and the real business cycle. American Economic Review 78(3): 402-417. – reference: Phillips PCB. 1991. Optimal inference in cointegrated systems. Econometrica 59(2): 283-306. – reference: Schmitt-Grohé S, Uribe M. 2012. Whats news in business cycles? Econometrica 80(6): 2733-2764. – reference: Geweke J. 1999. Using simulation methods for Bayesian econometric models: inference, development, and communication. Econometric Reviews 18(1): 1-126. – reference: Rabanal P, Rubio-Ramírez JF. 2005. Comparing New Keynesian models of the business cycle: a Bayesian approach. Journal of Monetary Economics 52(6): 1151-1166. – reference: Creal D. 2012. A survey of sequential Monte Carlo methods for economics and finance. Econometric Reviews 31(3): 245-296. – reference: King RG, Plosser CI, Rebelo S. 1988. Production, growth, and business cycles. I. The basic neoclassical model. Journal of Monetary Economics 21(2-3): 195-232. – reference: Del Moral P, Doucet A, Jasra A. 2012. On adaptive resampling strategies for sequential Monte Carlo methods. Bernoulli 18(1): 252-278. – reference: Otrok C. 2001. On measuring the welfare costs of business cycles. Journal of Monetary Economics 47(1): 61-92. – reference: Cappé O, Moulines E, Ryden T. 2005. Inference in Hidden Markov Models. Springer: Berlin. – reference: Chopin N. 2004. Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference. Annals of Statistics 32(6): 2385-2411. – reference: Del Negro M, Schorfheide F. 2008. Forming priors for DSGE models (and how it affects the assessment of nominal rigidities). Journal of Monetary Economics 55(7): 1191-1208. – reference: Strid I. 2010. Efficient parallelisation of Metropolis-Hastings algorithms using a prefetching approach. Computational Statistics and Data Analysis 54(11): 2814-2835. – reference: Chopin N. 2002. A sequential particle filter for static models. Biometrika 89(3): 539-551. – reference: Liu JS.2008. Monte Carlo Strategies in Scientific Computing. Springer: Berlin. – reference: Amdahl G. 1967. Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Conference Proceedings 30: 483-485. – reference: Schorfheide F. 2000. Loss function-based evaluation of DSGE models. Journal of Applied Econometrics 15:645-670. – reference: Smets F, Wouters R. 2007. Shocks and frictions in US business cycles: a Bayesian DSGE approach. American Economic Review 97:586-608. – reference: Jaimovich N, Rebelo S. 2009. Can news about the future drive the business cycle. American Economic Review 9(4): 1097-1118. – reference: DeJong DN, Ingram BF, Whiteman CH. 2000. A Bayesian approach to dynamic macroeconomics. Journal of Econometrics 98(2): 203-223. – reference: Geweke J. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6): 1317-1399. – reference: Chib S, Ramamurthy S. 2010. Tailored randomized block MCMC methods with application to DSGE models. Journal of Econometrics 155(1): 19-38. – reference: Creal D, Koopman SJ, Shephard N. 2009. Testing the assumptions behind importance sampling. 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Snippet	We develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle... SUMMARYWe develop a sequential Monte Carlo (SMC) algorithm for estimating Bayesian dynamic stochastic general equilibrium (DSGE) models; wherein a particle...
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SubjectTerms	Algorithms Bayesian analysis Bayesian method Dynamisches Gleichgewicht Economic models Economic shock Function words General economic equilibrium Inference Markov analysis Markovian processes Monte Carlo simulation Random walk theory Sampling Schätztheorie Stichprobenerhebung Stochastic models Stochastic processes Studies Theorie Working hours
Title	SEQUENTIAL MONTE CARLO SAMPLING FOR DSGE MODELS
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