IDEAS home Printed from https://ideas.repec.org/p/cpm/dynare/032.html
   My bibliography  Save this paper

Estimating nonlinear DSGE models with moments based methods

Author

Listed:
  • Sergey, Ivashchenko

Abstract

This article suggests the new approach to an approximation of nonlinear DSGE models moments. This approach is fast and accurate enough to use it for an estimation of nonlinear DSGE models. The small financial DSGE model is repeatedly estimated by several modifications of suggested approach. Approximations of moments are close to the results of large sample Monte Carlo estimation. Quality of parameters estimation with suggested approach is close to the Central Difference Kalman Filter (the CDKF) based. At the same time suggested approach is much faster.

Suggested Citation

  • Sergey, Ivashchenko, 2014. "Estimating nonlinear DSGE models with moments based methods," Dynare Working Papers 32, CEPREMAP.
  • Handle: RePEc:cpm:dynare:032
    as

    Download full text from publisher

    File URL: https://www.dynare.org/wp-repo/dynarewp032.pdf
    File Function: Main text
    Download Restriction: no

    File URL: https://www.dynare.org/wp-repo/dynarewp032.rar
    File Function: Source code of programs used in the paper
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    2. Collard, Fabrice & Juillard, Michel, 2001. "Accuracy of stochastic perturbation methods: The case of asset pricing models," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 979-999, June.
    3. Martin Møller Andreasen, 2008. "Non-linear DSGE Models, The Central Difference Kalman Filter, and The Mean Shifted Particle Filter," CREATES Research Papers 2008-33, Department of Economics and Business Economics, Aarhus University.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sergey Ivashchenko, 2014. "DSGE Model Estimation on the Basis of Second-Order Approximation," Computational Economics, Springer;Society for Computational Economics, vol. 43(1), pages 71-82, January.
    2. 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.
    3. Den Haan, Wouter J. & De Wind, Joris, 2012. "Nonlinear and stable perturbation-based approximations," Journal of Economic Dynamics and Control, Elsevier, vol. 36(10), pages 1477-1497.
    4. Özer Karagedikli & Troy Matheson & Christie Smith & Shaun P. Vahey, 2010. "RBCs AND DSGEs: THE COMPUTATIONAL APPROACH TO BUSINESS CYCLE THEORY AND EVIDENCE," Journal of Economic Surveys, Wiley Blackwell, vol. 24(1), pages 113-136, February.
    5. Gallic, Ewen & Vermandel, Gauthier, 2020. "Weather shocks," European Economic Review, Elsevier, vol. 124(C).
    6. Benigno, Gianluca & Benigno, Pierpaolo & Nisticò, Salvatore, 2013. "Second-order approximation of dynamic models with time-varying risk," Journal of Economic Dynamics and Control, Elsevier, vol. 37(7), pages 1231-1247.
    7. Sergey Ivashchenko, 2014. "Forecasting in a Non-Linear DSGE Model," EUSP Department of Economics Working Paper Series 2014/02, European University at St. Petersburg, Department of Economics.
    8. Gallic, Ewen & Vermandel, Gauthier, 2017. "Weather Shocks, Climate Change and Business Cycles," MPRA Paper 81230, University Library of Munich, Germany.
    9. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1439-1512, Elsevier.
    10. Alali, Walid Y., 2009. "Solution Strategies of Dynamic Stochastic General Equilibrium (DSGE) models," EconStor Preprints 269876, ZBW - Leibniz Information Centre for Economics.
    11. Alali, Walid Y., 2009. "Solution Strategies of Dynamic Stochastic General Equilibrium (DSGE) models," MPRA Paper 116480, University Library of Munich, Germany.
    12. Adnan Haider Bukhari & Safdar Ullah Khan, 2008. "A Small Open Economy DSGE Model for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 47(4), pages 963-1008.
    13. Pablo Burriel & Jesús Fernández-Villaverde & Juan Rubio-Ramírez, 2010. "MEDEA: a DSGE model for the Spanish economy," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 1(1), pages 175-243, March.
    14. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    15. Luca Benati & Paolo Surico, 2009. "VAR Analysis and the Great Moderation," American Economic Review, American Economic Association, vol. 99(4), pages 1636-1652, September.
    16. repec:zbw:bofrdp:2016_016 is not listed on IDEAS
    17. Kocięcki, Andrzej & Kolasa, Marcin, 2023. "A solution to the global identification problem in DSGE models," Journal of Econometrics, Elsevier, vol. 236(2).
    18. Mădălin Viziniuc, 2017. "Potential Gains from Cooperation Between Monetary and Macroprudential Policies: The Case of an Emerging Economy," Eastern European Economics, Taylor & Francis Journals, vol. 55(5), pages 420-452, September.
    19. Luca Benati & Paolo Surico, 2008. "Evolving U.S. Monetary Policy and The Decline of Inflation Predictability," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 634-646, 04-05.
    20. Eyal Argov & Emanuel Barnea & Alon Binyamini & Eliezer Borenstein & David Elkayam & Irit Rozenshtrom, 2012. "MOISE: A DSGE Model for the Israeli Economy," Bank of Israel Working Papers 2012.06, Bank of Israel.
    21. Posch, Olaf, 2009. "Structural estimation of jump-diffusion processes in macroeconomics," Journal of Econometrics, Elsevier, vol. 153(2), pages 196-210, December.

    More about this item

    Keywords

    DSGE; DSGE-VAR; GMM; nonlinear estimation;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cpm:dynare:032. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sébastien Villemot (email available below). General contact details of provider: https://edirc.repec.org/data/ceprefr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.