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Estimating Nonlinear DSGE Models by the Simulated Method of Moments

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  • RUGE-MURCIA, Francisco J.

Abstract

This paper studies the application of the simulated method of moments (SMM) for the estimation of nonlinear dynamic stochastic general equilibrium (DSGE) models. Monte Carlo analysis is employed to examine the small-sample properties of SMM in specifications with different curvature. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, asymptotic standard errors tend to overstate the actual variability of the estimates and, consequently, statistical inference is conservative. A simple strategy to incorporate priors in a method of moments context is proposed. An empirical application to the macroeconomic effects of rare events indicates that negatively skewed productivity shocks induce agents to accumulate additional capital and can endogenously generate asymmetric business cycles.

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File URL: https://papyrus.bib.umontreal.ca/jspui/handle/1866/4833
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Bibliographic Info

Paper provided by Universite de Montreal, Departement de sciences economiques in its series Cahiers de recherche with number 2010-10.

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Length: 59 pages
Date of creation: 2010
Date of revision:
Handle: RePEc:mtl:montde:2010-10

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Keywords: Monte-Carlo analysis; priors; perturbation methods; rare events; skewness.;

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References

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  1. Fabio Canova & Luca Sala, 2005. "Back to square one: Identification issues in DSGE models," Economics Working Papers 927, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2006.
  2. Hamilton, James D, 1991. "A Quasi-Bayesian Approach to Estimating Parameters for Mixtures of Normal Distributions," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(1), pages 27-39, January.
  3. Christopher A. Sims & Jinill Kim & Sunghyun Kim, 2003. "Calculating and Using Second Order Accurate Solution of Discrete Time Dynamic Equilibrium Models," Computing in Economics and Finance 2003 162, Society for Computational Economics.
  4. Serena Ng & Pierre Perron, 1997. "Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power," Boston College Working Papers in Economics 369, Boston College Department of Economics, revised 01 Sep 2000.
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  7. Lombardo, Giovanni, 2010. "On approximating DSGE models by series expansions," Working Paper Series 1264, European Central Bank.
  8. Manuel S. Santos & Adrian Peralta-Alva, 2003. "Accuracy Of Simulations For Stochastic Dynamic Models," Economics Working Papers we034615, Universidad Carlos III, Departamento de Economía.
  9. Francisco J. Ruge-Murcia, 2004. "Methods to Estimate Dynamic Stochastic General Equilibrium Models," 2004 Meeting Papers 83, Society for Economic Dynamics.
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  12. Barro, Robert, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," Scholarly Articles 3208215, Harvard University Department of Economics.
  13. Daniel McFadden, 1987. "A Method of Simulated Moments for Estimation of Discrete Response Models Without Numerical Integration," Working papers 464, Massachusetts Institute of Technology (MIT), Department of Economics.
  14. KIM, Jinill & RUGE-MURCIA, Francisco J., 2007. "How Much Inflation is Necessary to Grease the Wheels?," Cahiers de recherche 11-2007, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  15. Smith, A A, Jr, 1993. "Estimating Nonlinear Time-Series Models Using Simulated Vector Autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S63-84, Suppl. De.
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  18. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
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Cited by:
  1. Creel, Michael & Kristensen, Dennis, 2011. "Indirect Likelihood Inference," Dynare Working Papers 8, CEPREMAP.
  2. Caterina Mendicino, 2012. "Collateral Requirements: Macroeconomic Fluctuations and Macro-Prudential Policy," Working Papers w201211, Banco de Portugal, Economics and Research Department.
  3. Benjamin Born & Johannes Pfeifer, 2013. "Policy Risk and the Business Cycle," CESifo Working Paper Series 4336, CESifo Group Munich.
  4. KIM, Jinill & RUGE-MURCIA, Francisco J., 2009. "Monetary Policy When Wages Are Downwardly Rigid: Friedman Meets Tobin," Cahiers de recherche 2009-14, Universite de Montreal, Departement de sciences economiques.
  5. Hong Lan & Alexander Meyer-Gohde, 2013. "Pruning in Perturbation DSGE Models - Guidance from Nonlinear Moving Average Approximations," SFB 649 Discussion Papers SFB649DP2013-024, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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