IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v197y2020ics0165176520303840.html
   My bibliography  Save this article

Estimating nonlinear dynamic equilibrium models by matching impulse responses

Author

Listed:
  • Ruge-Murcia, Francisco

Abstract

This paper examines the proposition that using a nonlinear – instead of a linear – auxiliary model for the indirect inference estimation of a nonlinear dynamic equilibrium model should deliver more efficient estimates and statistical inference. Focusing on the widely-used impulse-response matching procedure, it is pointed out that a nonlinear dynamic equilibrium model generates impulse responses that depend on the sign, size, and timing of the shock. This is also the case for impulse responses generated by a nonlinear auxiliary model. In contrast, impulse responses generated by a linear auxiliary model are independent of the sign, size, and timing of the shock. Monte-Carlo results show that both auxiliary models deliver estimates close to their true values, but that using a nonlinear auxiliary model yields more efficient estimates because it exploits information on the mean of the variables and the curvature of the economic model.

Suggested Citation

  • Ruge-Murcia, Francisco, 2020. "Estimating nonlinear dynamic equilibrium models by matching impulse responses," Economics Letters, Elsevier, vol. 197(C).
  • Handle: RePEc:eee:ecolet:v:197:y:2020:i:c:s0165176520303840
    DOI: 10.1016/j.econlet.2020.109624
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176520303840
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2020.109624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Julio J. Rotemberg & Michael Woodford, 1998. "An Optimization-Based Econometric Framework for the Evaluation of Monetary Policy: Expanded Version," NBER Technical Working Papers 0233, National Bureau of Economic Research, Inc.
    2. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    3. Matteo Iacoviello, 2005. "House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle," American Economic Review, American Economic Association, vol. 95(3), pages 739-764, June.
    4. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, April.
    5. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    6. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2011. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(2), pages 225-247, April.
    7. Riccardo DiCecio & Edward Nelson, 2007. "An estimated DSGE model for the United Kingdom," Review, Federal Reserve Bank of St. Louis, vol. 89(Jul), pages 215-232.
    8. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    9. Òscar Jordà & Sharon Kozicki, 2011. "Estimation And Inference By The Method Of Projection Minimum Distance: An Application To The New Keynesian Hybrid Phillips Curve," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(2), pages 461-487, May.
    10. 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.
    11. Dridi, Ramdan & Guay, Alain & Renault, Eric, 2007. "Indirect inference and calibration of dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 136(2), pages 397-430, February.
    12. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    13. Martin Andreasen, 2012. "On the Effects of Rare Disasters and Uncertainty Shocks for Risk Premia in Non-Linear DSGE Models," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 15(3), pages 295-316, July.
    14. Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1993. "Nonlinear Dynamic Structures," Econometrica, Econometric Society, vol. 61(4), pages 871-907, July.
    15. Barnichon, Regis & Matthes, Christian, 2018. "Functional Approximation of Impulse Responses," Journal of Monetary Economics, Elsevier, vol. 99(C), pages 41-55.
    16. Robert J. Barro, 2006. "Rare Disasters and Asset Markets in the Twentieth Century," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(3), pages 823-866.
    17. Schmitt-Grohe, Stephanie & Uribe, Martin, 2004. "Solving dynamic general equilibrium models using a second-order approximation to the policy function," Journal of Economic Dynamics and Control, Elsevier, vol. 28(4), pages 755-775, January.
    18. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    19. 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 63-84, Suppl. De.
    20. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    21. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    22. Levintal, Oren, 2017. "Fifth-order perturbation solution to DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 80(C), pages 1-16.
    23. David Altig & Lawrence Christiano & Martin Eichenbaum & Jesper Linde, 2011. "Firm-Specific Capital, Nominal Rigidities and the Business Cycle," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(2), pages 225-247, April.
    24. Lutz Kilian & Yun Jung Kim, 2011. "How Reliable Are Local Projection Estimators of Impulse Responses?," The Review of Economics and Statistics, MIT Press, vol. 93(4), pages 1460-1466, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christophe Gouel & Nicolas Legrand, 2022. "The Role of Storage in Commodity Markets: Indirect Inference Based on Grains Data," Working Papers 2022-04, CEPII research center.
    2. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.

    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. 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.
    2. Hall, Alastair R. & Inoue, Atsushi & Nason, James M. & Rossi, Barbara, 2012. "Information criteria for impulse response function matching estimation of DSGE models," Journal of Econometrics, Elsevier, vol. 170(2), pages 499-518.
    3. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    4. Guerron-Quintana, Pablo & Inoue, Atsushi & Kilian, Lutz, 2017. "Impulse response matching estimators for DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 144-155.
    5. Martin M Andreasen & Jesús Fernández-Villaverde & Juan F Rubio-Ramírez, 2018. "The Pruned State-Space System for Non-Linear DSGE Models: Theory and Empirical Applications," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 85(1), pages 1-49.
    6. Castelnuovo, Efrem & Pellegrino, Giovanni, 2018. "Uncertainty-dependent effects of monetary policy shocks: A new-Keynesian interpretation," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 277-296.
    7. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    8. Giraitis, Liudas & Kapetanios, George & Theodoridis, Konstantinos & Yates, Tony, 2014. "Estimating time-varying DSGE models using minimum distance methods," Bank of England working papers 507, Bank of England.
    9. Òscar Jordà & Alan M. Taylor, 2024. "Local Projections," NBER Working Papers 32822, National Bureau of Economic Research, Inc.
    10. 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.
    11. Anthony M. Diercks & Alex Hsu & Andrea Tamoni, 2020. "When it Rains it Pours: Cascading Uncertainty Shocks," Finance and Economics Discussion Series 2020-064, Board of Governors of the Federal Reserve System (U.S.).
    12. RUGE-MURCIA, Francisco J., 2010. "Estimating Nonlinear DSGE Models by the Simulated Method of Moments," Cahiers de recherche 2010-10, Universite de Montreal, Departement de sciences economiques.
    13. Mumtaz, Haroon & Theodoridis, Konstantinos, 2020. "Dynamic effects of monetary policy shocks on macroeconomic volatility," Journal of Monetary Economics, Elsevier, vol. 114(C), pages 262-282.
    14. Koursaros, Demetris, 2017. "Labor market dynamics when (un)employment is a social norm," Journal of Economic Behavior & Organization, Elsevier, vol. 134(C), pages 96-116.
    15. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    16. Martin Kliem & Alexander Meyer‐Gohde, 2022. "(Un)expected monetary policy shocks and term premia," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 477-499, April.
    17. Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2020. "Uncertainty and Monetary Policy during Extreme Events," CESifo Working Paper Series 8561, CESifo.
    18. Iania, Leonardo & Tretiakov, Pavel & Wouters, Rafael, 2023. "The risk premium in New Keynesian DSGE models: The cost of inflation channel," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    19. Poghosyan, Karen & Boldea, Otilia, 2013. "Structural versus matching estimation: Transmission mechanisms in Armenia," Economic Modelling, Elsevier, vol. 30(C), pages 136-148.
    20. Angelini, Giovanni & Sorge, Marco M., 2021. "Under the same (Chole)sky: DNK models, timing restrictions and recursive identification of monetary policy shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 133(C).

    More about this item

    Keywords

    Local projections; Indirect inference; Nonlinear models; Rare disasters; DGSE;
    All these keywords.

    JEL classification:

    • 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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:eee:ecolet:v:197:y:2020:i:c:s0165176520303840. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.