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Assessing Identifying Restrictions in SVAR Models

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  • Michele Piffer

Abstract

This paper proposes a Bayesian approach to assess if the data support candidate set-identifying restrictions for Vector Autoregressive models. The researcher is uncertain about the validity of some sign restrictions that she is contemplating to use. She therefore expresses her uncertainty with a prior distribution that covers the parameter space both where the restrictions are satisfied and where they are not satisfied. I show that the data determine whether the probability mass in favour of the restrictions increases or not from prior to posterior. Using two applications, I find support for the restrictions used by Baumeister & Hamilton (2015a) in their two-equation model of labor demand and supply, and I find support for the true data generating process in a simulation exercise on the New Keynesian model.

Suggested Citation

  • Michele Piffer, 2016. "Assessing Identifying Restrictions in SVAR Models," Discussion Papers of DIW Berlin 1563, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1563
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    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.529759.de/dp1563.pdf
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    References listed on IDEAS

    as
    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Paustian Matthias, 2007. "Assessing Sign Restrictions," The B.E. Journal of Macroeconomics, De Gruyter, vol. 7(1), pages 1-33, August.
    3. Roland Straub & Gert Peersman, 2006. "Putting the New Keynesian Model to a Test," IMF Working Papers 06/135, International Monetary Fund.
    4. Lutz Kilian & Daniel P. Murphy, 2012. "Why Agnostic Sign Restrictions Are Not Enough: Understanding The Dynamics Of Oil Market Var Models," Journal of the European Economic Association, European Economic Association, vol. 10(5), pages 1166-1188, October.
    5. Kociecki, Andrzej, 2013. "Bayesian Approach and Identification," MPRA Paper 46538, University Library of Munich, Germany.
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    More about this item

    Keywords

    Identification; Bayesian econometrics; sign restrictions;

    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
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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