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Estimating Nonlinear DSGE Models by the Simulated Method of Moments

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  • Francisco J. Ruge-Murcia

    (University of Montreal)

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.

Suggested Citation

  • Francisco J. Ruge-Murcia, 2011. "Estimating Nonlinear DSGE Models by the Simulated Method of Moments," 2011 Meeting Papers 237, Society for Economic Dynamics.
  • Handle: RePEc:red:sed011:237
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    Citations

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    Cited by:

    1. Cantore, C. & Levine, P., 2012. "Getting normalization right: Dealing with ‘dimensional constants’ in macroeconomics," Journal of Economic Dynamics and Control, Elsevier, pages 1931-1949.
    2. Kim, Jinill & Ruge-Murcia, Francisco J., 2011. "Monetary policy when wages are downwardly rigid: Friedman meets Tobin," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2064-2077.
    3. Born, Benjamin & Pfeifer, Johannes, 2014. "Policy risk and the business cycle," Journal of Monetary Economics, Elsevier, pages 68-85.
    4. Michael Creel & Dennis Kristensen, "undated". "Indirect Likelihood Inference," Working Papers 558, Barcelona Graduate School of Economics.
    5. Ercolani, Valerio & Valle e Azevedo, João, 2014. "The effects of public spending externalities," Journal of Economic Dynamics and Control, Elsevier, pages 173-199.
    6. Francisco J. Ruge-Murcia, 2013. "Generalized Method of Moments estimation of DSGE models," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 20, pages 464-485 Edward Elgar Publishing.
    7. Born, Benjamin & Pfeifer, Johannes, 2014. "Policy risk and the business cycle," Journal of Monetary Economics, Elsevier, vol. 68(C), pages 68-85.
    8. 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.
    9. Lan, Hong & Meyer-Gohde, Alexander, 2013. "Solving DSGE models with a nonlinear moving average," Journal of Economic Dynamics and Control, Elsevier, pages 2643-2667.
    10. Andrew Binning, 2013. "Third-order approximation of dynamic models without the use of tensors," Working Paper 2013/13, Norges Bank.
    11. Andrew Binning, 2013. "Solving second and third-order approximations to DSGE models: A recursive Sylvester equation solution," Working Paper 2013/18, Norges Bank.

    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment

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