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Underidentified SVAR models: A framework for combining short and long-run restrictions with sign-restrictions

  • Andrew Binning


    (Norges Bank (Central Bank of Norway))

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    I describe a new method for imposing zero restrictions (both short and long-run) in combination with conventional sign-restrictions. In particular I extend the Rubio-Ramirez et al.(2010) algorithm for applying short and long-run restrictions for exactly identified models to models that are underidentified. In turn this can be thought of as a unifying framework for short-run, long-run and sign restrictions. I demonstrate my algorithm with two examples. In the first example I estimate a VAR model using the Smets & Wouters (2007) dataset and impose sign and zero restrictions based on the impulse responses from their DSGE model. In the second example I estimate a BVAR model using the Mountford & Uhlig (2009) data set and impose the same sign and zero restrictions they use to identify an anticipated government revenue shock.

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    Paper provided by Norges Bank in its series Working Paper with number 2013/14.

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    Length: 28 pages
    Date of creation: 10 Jun 2013
    Date of revision:
    Handle: RePEc:bno:worpap:2013_14
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    1. Frank Smets & Raf Wouters, 2007. "Shocks and Frictions in US Business Cycles : a Bayesian DSGE Approach," Working Paper Research 109, National Bank of Belgium.
    2. Blanchard, Olivier Jean & Quah, Danny, 1989. "The Dynamic Effects of Aggregate Demand and Supply Disturbances," American Economic Review, American Economic Association, vol. 79(4), pages 655-73, September.
    3. Ren�e Fry & Adrian Pagan, 2011. "Sign Restrictions in Structural Vector Autoregressions: A Critical Review," Journal of Economic Literature, American Economic Association, vol. 49(4), pages 938-60, December.
    4. Christiane Baumeister & Luca Benati, 2013. "Unconventional Monetary Policy and the Great Recession: Estimating the Macroeconomic Effects of a Spread Compression at the Zero Lower Bound," International Journal of Central Banking, International Journal of Central Banking, vol. 9(2), pages 165-212, June.
    5. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions," Journal of the European Economic Association, MIT Press, vol. 4(2-3), pages 475-483, 04-05.
    6. Juan F. Rubio-Ramírez & Daniel F.Waggoner & Tao Zha, 2008. "Structural vector autoregressions: theory of identification and algorithms for inference," FRB Atlanta Working Paper No. 2008-18, Federal Reserve Bank of Atlanta.
    7. Gali, Jordi, 1992. "How Well Does the IS-LM Model Fit Postwar U.S. Data," The Quarterly Journal of Economics, MIT Press, vol. 107(2), pages 709-38, May.
    8. Faust, Jon, 1998. "The robustness of identified VAR conclusions about money," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 49(1), pages 207-244, December.
    9. Fabio Canova & Gianni De Nicolo, 2000. "Monetary disturbances matter for business fluctuations in the G-7," International Finance Discussion Papers 660, Board of Governors of the Federal Reserve System (U.S.).
    10. Jon Faust, 1998. "The robustness of identified VAR conclusions about money," International Finance Discussion Papers 610, Board of Governors of the Federal Reserve System (U.S.).
    11. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    12. Andrew Mountford & Harald Uhlig, 2008. "What are the Effects of Fiscal Policy Shocks?," NBER Working Papers 14551, National Bureau of Economic Research, Inc.
    13. Liu, Philip & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2011. "International transmission of shocks: a time-varying factor-augmented VAR approach to the open economy," Bank of England working papers 425, Bank of England.
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