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Investigating the Relationship Between DSGE and SVAR Models

Author

Listed:
  • Adrian Pagan

    () (UniMelb)

  • Tim Robinson

    (UniMelb)

Abstract

DSGE models often contain variables for which data is not observed when estimating. Although DSGE models generally imply that there is a finite order SVAR in all the variables this may no longer be true for SVARs just in observable variables, and so there is a VAR-truncation problem. The paper examines this issue. It looks at five different studies using DSGE models that appear in the literature. Generally it emerges that the truncation issue is probably not that important, except possibly in small open economy models with external debt. Even when there is no truncation problem in VARs which control the dynamics) the structural impulse responses from both models may be different due to differing initial responses. It is shown that DSGE models incorporate some strong restrictions on the nature of SVAR models and these would need to employed for the two approaches to give the same initial estimates.

Suggested Citation

  • Adrian Pagan & Tim Robinson, 2016. "Investigating the Relationship Between DSGE and SVAR Models," NCER Working Paper Series 112, National Centre for Econometric Research.
  • Handle: RePEc:qut:auncer:2016_03
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    File URL: http://www.ncer.edu.au/papers/documents/WP112.pdf
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    References listed on IDEAS

    as
    1. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez & Thomas J. Sargent & Mark W. Watson, 2007. "ABCs (and Ds) of Understanding VARs," American Economic Review, American Economic Association, vol. 97(3), pages 1021-1026, June.
    2. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    3. Philip Liu & Konstantinos Theodoridis, 2012. "DSGE Model Restrictions for Structural VAR Identification," International Journal of Central Banking, International Journal of Central Banking, vol. 8(4), pages 61-95, December.
    4. Christopher J. Erceg & Luca Guerrieri & Christopher Gust, 2005. "Can Long-Run Restrictions Identify Technology Shocks?," Journal of the European Economic Association, MIT Press, vol. 3(6), pages 1237-1278, December.
    5. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    6. Alejandro Justiniano & Bruce Preston, 2010. "Monetary policy and uncertainty in an empirical small open-economy model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 93-128.
    7. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    8. Franchi, Massimo & Vidotto, Anna, 2013. "A check for finite order VAR representations of DSGE models," Economics Letters, Elsevier, vol. 120(1), pages 100-103.
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    Cited by:

    1. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    2. Xianglong Liu & Adrian R. Pagan & Tim Robinson, 2018. "Critically Assessing Estimated DSGE Models: A Case Study of a Multi‐sector Model," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 349-371, December.

    More about this item

    Keywords

    Impulse Responses to DSGE; SVAR;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical

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