IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v146y2016icp77-81.html
   My bibliography  Save this article

A note on the Cogley–Nason–Sims approach

Author

Abstract

In evaluating an economic model with Structural Vector Auto-Regression (SVAR), the Cogley–Nason–Sims (CNS) approach compares impulse responses estimated from empirical data with those obtained from the identical SVAR run on model generated data. Using Monte-Carlo simulations, this paper examines small sample performance of the CNS approach.

Suggested Citation

  • Hussain, Syed M. & Liu, Lin, 2016. "A note on the Cogley–Nason–Sims approach," Economics Letters, Elsevier, vol. 146(C), pages 77-81.
    • Handle: RePEc:eee:ecolet:v:146:y:2016:i:c:p:77-81
    DOI: 10.1016/j.econlet.2016.06.036
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176516302403
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2016.06.036?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. Cogley, Timothy & Nason, James M, 1995. "Output Dynamics in Real-Business-Cycle Models," American Economic Review, American Economic Association, vol. 85(3), pages 492-511, June.
    2. Karel Mertens & Morten Overgaard Ravn, 2011. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54, January.
    3. Christopher A. Sims, 1989. "Models and Their Uses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 489-494.
    4. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2011. "How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2078-2104.
    5. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106, National Bureau of Economic Research, Inc.
    6. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2004. "A Critique of Structural VARs Using Real Business Cycle Theory," Levine's Bibliography 122247000000000518, UCLA Department of Economics.
    7. 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.
    8. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    9. Carlstrom, Charles T. & Fuerst, Timothy S. & Paustian, Matthias, 2009. "Monetary policy shocks, Choleski identification, and DNK models," Journal of Monetary Economics, Elsevier, vol. 56(7), pages 1014-1021, October.
    10. Efrem Castelnuovo & Paolo Surico, 2010. "Monetary Policy, Inflation Expectations and The Price Puzzle," Economic Journal, Royal Economic Society, vol. 120(549), pages 1262-1283, December.
    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. Giuliano Curatola & Michael Donadelli & Patrick Gruning & Christoph Meinerding, 2016. "Investment-Specific Shocks, Business Cycles, and Asset Prices," Bank of Lithuania Working Paper Series 36, Bank of Lithuania.

    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. Liu, Lin & Hussain, Syed, 2013. "Understanding the Sims-Cogley-Nason Approach in A Finite Sample," MPRA Paper 53118, University Library of Munich, Germany.
    2. Martial Dupaigne & Patrick Feve & Julien Matheron, 2007. "Technology Shocks, Non-stationary Hours and DSVAR," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 10(2), pages 238-255, April.
    3. Anna Mikusheva, 2014. "Estimation of dynamic stochastic general equilibrium models (in Russian)," Quantile, Quantile, issue 12, pages 1-21, February.
    4. Ramey, V.A., 2016. "Macroeconomic Shocks and Their Propagation," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 71-162, Elsevier.
    5. 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.
    6. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    7. Efrem Castelnuovo, 2016. "Monetary policy shocks and Cholesky VARs: an assessment for the Euro area," Empirical Economics, Springer, vol. 50(2), pages 383-414, March.
    8. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.
    9. Karel Mertens & Morten Overgaard Ravn, 2011. "Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 27-54, January.
    10. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
    11. Marek Rusnak & Tomas Havranek & Roman Horvath, 2013. "How to Solve the Price Puzzle? A Meta‐Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(1), pages 37-70, February.
    12. Forni, Lorenzo & Monteforte, Libero & Sessa, Luca, 2009. "The general equilibrium effects of fiscal policy: Estimates for the Euro area," Journal of Public Economics, Elsevier, vol. 93(3-4), pages 559-585, April.
    13. Ida Wolden Bache, 2008. "Assessing estimates of the exchange rate pass-through," Working Paper 2007/12, Norges Bank.
    14. Hussain, Syed M. & Malik, Samreen, 2016. "Asymmetric Effects of Exogenous Tax Changes," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 268-300.
    15. Hussain, Syed Muhammad, 2015. "The contractionary effects of tax shocks on productivity: An empirical and theoretical analysis," Journal of Macroeconomics, Elsevier, vol. 43(C), pages 93-107.
    16. Florio, Anna, 2018. "Nominal anchors and the price puzzle," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 224-237.
    17. Kuan‐Jen Chen & Ching‐Chong Lai, 2015. "On‐the‐Job Learning and News‐Driven Business Cycles," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 261-294, March.
    18. 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.
    19. Guido Lorenzoni, 2009. "A Theory of Demand Shocks," American Economic Review, American Economic Association, vol. 99(5), pages 2050-2084, December.
    20. Castelnuovo, Efrem, 2016. "Modest macroeconomic effects of monetary policy shocks during the great moderation: An alternative interpretation," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 300-314.

    More about this item

    Keywords

    Cogley–Nason–Sims approach; Small sample properties; Structural Vector Auto-Regression; Identification; Monte-Carlo simulation;
    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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    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:146:y:2016:i:c:p:77-81. 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.