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Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles


  • Ruge-Murcia, Francisco


This paper studies the application of the simulated method of moments (SMM) to 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 curvatures and departures from certainty equivalence. Results show that SMM is computationally efficient and delivers accurate estimates, even when the simulated series are relatively short. However, the small-sample distribution of the estimates is not always well approximated by the asymptotic Normal distribution. An empirical application to the macroeconomic effects of skewed disturbances shows that negatively skewed productivity shocks induce agents to accumulate additional capital and can generate asymmetric business cycles.

Suggested Citation

  • Ruge-Murcia, Francisco, 2012. "Estimating nonlinear DSGE models by the simulated method of moments: With an application to business cycles," Journal of Economic Dynamics and Control, Elsevier, vol. 36(6), pages 914-938.
  • Handle: RePEc:eee:dyncon:v:36:y:2012:i:6:p:914-938 DOI: 10.1016/j.jedc.2012.01.008

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    References listed on IDEAS

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    Cited by:

    1. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2015. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 126-154.
    2. Francisco Ruge-Murcia, 2012. "Skewness Risk and Bond Prices," Cahiers de recherche 17-2012, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    3. Born, Benjamin & Pfeifer, Johannes, 2014. "Policy risk and the business cycle," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 68-85.
    4. Julien Albertini & Hong Lan, 2016. "The importance of time-varying parameters in new Keynesian models with zero lower bound," SFB 649 Discussion Papers SFB649DP2016-013, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Brzoza-Brzezina, Michał & Kolasa, Marcin & Makarski, Krzysztof, 2017. "Monetary and macroprudential policy with foreign currency loans," Journal of Macroeconomics, Elsevier, vol. 54(PB), pages 352-372.
    6. Michael Creel & Jiti Gao & Han Hong & Dennis Kristensen, 2016. "Bayesian Indirect Inference and the ABC of GMM," Monash Econometrics and Business Statistics Working Papers 1/16, Monash University, Department of Econometrics and Business Statistics.
    7. repec:eee:macchp:v2-527 is not listed on IDEAS
    8. Jinill KIM & Francisco RUGE-MURCIA, 2016. "Extreme Events and Optimal Monetary Policy," Cahiers de recherche 09-2016, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    9. Alexandre Gohin & Yu Zheng, 2016. "Assessing the Decoupling of EU Agricultural Policy on Farm Decisions - A Dynamic Stochastic Attempt," FOODSECURE Working papers 45, LEI Wageningen UR.
    10. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, Elsevier.
    11. repec:eee:dyncon:v:80:y:2017:i:c:p:1-16 is not listed on IDEAS
    12. Martin M. Andreasen & Anders Kronborg, 2803. "The Extended Perturbation Method: New Insights on the New Keynesian Model," CREATES Research Papers 2017-14, Department of Economics and Business Economics, Aarhus University.
    13. Reiner Franke, 2015. "How Fat-Tailed is US Output Growth?," Metroeconomica, Wiley Blackwell, vol. 66(2), pages 213-242, May.
    14. Philipp Eisenhauer & James J. Heckman & Stefano Mosso, 2015. "Estimation Of Dynamic Discrete Choice Models By Maximum Likelihood And The Simulated Method Of Moments," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 331-357, May.
    15. Yuanyuan Chen & Stuart Fowler, 2016. "Hybrid Perturbation-Projection Method for Solving DSGE Asset Pricing Models," Computational Economics, Springer;Society for Computational Economics, vol. 48(4), pages 649-667, December.
    16. Francisco Blasques, 2013. "Solution-Driven Specification of DSGE Models," Tinbergen Institute Discussion Papers 13-062/III, Tinbergen Institute.
    17. Sergey Ivashchenko, 2014. "Forecasting in a Non-Linear DSGE Model," EUSP Department of Economics Working Paper Series Ec-02/14, European University at St. Petersburg, Department of Economics.
    18. Martin M. Andreasen & Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2013. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications," NBER Working Papers 18983, National Bureau of Economic Research, Inc.
    19. Lan, Hong & Meyer-Gohde, Alexander, 2013. "Solving DSGE models with a nonlinear moving average," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2643-2667.
    20. Lan, Hong & Meyer-Gohde, Alexander, 2014. "Solvability of perturbation solutions in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 45(C), pages 366-388.
    21. Michael Creel & Dennis Kristensen, 2013. "Indirect Likelihood Inference (revised)," UFAE and IAE Working Papers 931.13, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    22. Jinill, Kim & Ruge-Murcia, Francisco, 2018. "Extreme events and optimal monetary policy," Research Discussion Papers 4/2018, Bank of Finland.
    23. Dongya Koh & Raül Santaeulàlia-Llopis, 2017. "Countercyclical Elasticity of Substitution," Working Papers 946, Barcelona Graduate School of Economics.
    24. Francisco RUGE-MURCIA, 2014. "Indirect Inference Estimation of Nonlinear Dynamic General Equilibrium Models : With an Application to Asset Pricing under Skewness Risk," Cahiers de recherche 15-2014, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    25. repec:eee:eecrev:v:102:y:2018:i:c:p:211-239 is not listed on IDEAS

    More about this item


    Monte-Carlo analysis; Method of moments; Perturbation methods; Skewness; Asymmetric shocks;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment


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