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

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  • Ruge-Murcia, Francisco

Abstract

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.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 36 (2012)
Issue (Month): 6 ()
Pages: 914-938

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Handle: RePEc:eee:dyncon:v:36:y:2012:i:6:p:914-938

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Web page: http://www.elsevier.com/locate/jedc

Related research

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

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Citations

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Cited by:
  1. Hong Lan & Alexander Meyer-Gohde, 2011. "Solving DSGE Models with a Nonlinear Moving Average," SFB 649 Discussion Papers SFB649DP2011-087, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Andreasen, Martin M. & Fernández-Villaverde, Jesús & Rubio-Ramírez, Juan Francisco, 2013. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications," CEPR Discussion Papers 9442, C.E.P.R. Discussion Papers.
  3. Francisco Ruge-Murcia, 2012. "Skewness Risk and Bond Prices," Cahiers de recherche 17-2012, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
  4. Francisco Blasques, 2013. "Solution-Driven Specification of DSGE Models," Tinbergen Institute Discussion Papers 13-062/III, Tinbergen Institute.
  5. Francisco Blasques, 2013. "Solution-Driven Specification of DSGE Models," Tinbergen Institute Discussion Papers 13-062/III, Tinbergen Institute.
  6. Sacht, Stephen & Franke, Reiner & Jang, Tae-Seok, 2013. "Moment Matching versus Bayesian Estimation: Backward-Looking Behaviour in a New-Keynesian Baseline Model," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79694, Verein für Socialpolitik / German Economic Association.
  7. 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).
  8. Sergey Ivashchenko, 2014. "Forecasting In a Non-Linear DSGE Model," EUSP Deparment of Economics Working Paper Series Ec-02/14, European University at St. Petersburg, Department of Economics.

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