IDEAS home Printed from https://ideas.repec.org/h/pal/palchp/978-0-230-24440-5_2.html
   My bibliography  Save this book chapter

How much Structure in Empirical Models?

In: Palgrave Handbook of Econometrics

Author

Listed:
  • Fabio Canova

Abstract

This chapter highlights the problems that structural methods and SVAR approaches have when estimating DSGE models and examining their ability to capture important features of the data. We show that structural methods are subject to severe identification problems due, in large part, to the nature of DSGE models. The problems can be patched up in a number of ways, but solved only if DSGEs are completely reparameterized or respecified. The potential misspecification of the structural relationships gives Bayesian methods an edge over classical ones in structural estimation. SVAR approaches may face invertibility problems but simple diagnostics can help to detect and remedy these problems. A pragmatic empirical approach ought to use the flexibility of SVARs against potential misspecification of the structural relationships but must firmly tie SVARs to the class of DSGE models which could have generated the data.

Suggested Citation

  • Fabio Canova, 2009. "How much Structure in Empirical Models?," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 2, pages 68-97, Palgrave Macmillan.
    • Handle: RePEc:pal:palchp:978-0-230-24440-5_2
    DOI: 10.1057/9780230244405_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

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


    Cited by:

    1. Leon Podkaminer, 2021. "Dynamic Stochastic General Equilibrium: macroeconomics at a dead end," Bank i Kredyt, Narodowy Bank Polski, vol. 52(2), pages 97-122.
    2. Horacio A. Aguirre & Emilio F. Blanco, 2015. "Credit and Macroprudential Policy in an Emerging Economy: a Structural Model Assessment," BIS Working Papers 504, Bank for International Settlements.
    3. Jean-Sébastien Pentecôte, 2010. "Long-run identifying restrictions on VARs within the AS-AD framework," Post-Print halshs-00554867, HAL.
    4. Marco Cozzi, 2014. "Heterogeneity In Macroeconomics And The Minimal Econometric Interpretation For Model Comparison," Working Paper 1333, Economics Department, Queen's University.
    5. Pablo Cuba‐Borda & Luca Guerrieri & Matteo Iacoviello & Molin Zhong, 2019. "Likelihood evaluation of models with occasionally binding constraints," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1073-1085, November.
    6. Niraj Poudyal & Aris Spanos, 2022. "Model Validation and DSGE Modeling," Econometrics, MDPI, vol. 10(2), pages 1-25, April.
    7. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    8. Fabio Canova & Filippo Ferroni, 2011. "Multiple filtering devices for the estimation of cyclical DSGE models," Quantitative Economics, Econometric Society, vol. 2(1), pages 73-98, March.
    9. Grazzini, Jakob & Richiardi, Matteo, 2015. "Estimation of ergodic agent-based models by simulated minimum distance," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 148-165.
    10. Duo Qin, 2022. "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers 249, Department of Economics, SOAS University of London, UK.

    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:pal:palchp:978-0-230-24440-5_2. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    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.