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Measuring monetary policy with empirically grounded identifying restrictions

  • Piyachart Phiromswad

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    This article reevaluates the impulse response functions (IRFs) to a monetary policy shock of the structural vector autoregression (SVAR). Identifying restrictions are specified and justified based on empirical evidence,i.e., conditional independence relations of variables, which is an important dimension that a good model must be able to mimic. The empirical-based approach is able to significant narrow down the set of admissible causal orders to identify the IRFs to a monetary policy shock (from 2,482 to 8). I find that most of the qualitative “stylized” features reported in the literature remain intact. However, the quantitative predictions are much less certain than what is commonly perceived. Copyright Springer-Verlag Berlin Heidelberg 2014

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    File URL: http://hdl.handle.net/10.1007/s00181-013-0692-7
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    Article provided by Springer in its journal Empirical Economics.

    Volume (Year): 46 (2014)
    Issue (Month): 2 (March)
    Pages: 681-699

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    Handle: RePEc:spr:empeco:v:46:y:2014:i:2:p:681-699
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    1. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    2. Sims, Christopher A. & Zha, Tao, 2006. "Does Monetary Policy Generate Recessions?," Macroeconomic Dynamics, Cambridge University Press, vol. 10(02), pages 231-272, April.
    3. Strongin, Steven, 1995. "The identification of monetary policy disturbances explaining the liquidity puzzle," Journal of Monetary Economics, Elsevier, vol. 35(3), pages 463-497, June.
    4. Bjørnland , Hilde & Leitemo, Kai, 2005. "Identifying the interdependence between US monetary policy and the stock market," Research Discussion Papers 17/2005, Bank of Finland.
    5. Ben S. Bernanke & Martin L. Parkinson, 1990. "Procyclical Labor Productivity and Competing Theories of the Business Cycle: Some Evidence from Interwar U.S. Manufacturing Industries," NBER Working Papers 3503, National Bureau of Economic Research, Inc.
    6. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," NBER Working Papers 8403, National Bureau of Economic Research, Inc.
    7. Christopher J. Erceg & Luca Guerrieri & Christopher J. Gust, 2004. "Can long-run restrictions identify technology shocks?," International Finance Discussion Papers 792, Board of Governors of the Federal Reserve System (U.S.).
    8. Lippi, Marco & Reichlin, Lucrezia, 1993. "Diffusion of Technical Change and the Decomposition of Output into Trend and Cycle," CEPR Discussion Papers 775, C.E.P.R. Discussion Papers.
    9. James G. MacKinnon, 1995. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Working Papers 918, Queen's University, Department of Economics.
    10. Christopher A. Sims, 1992. "Interpreting the Macroeconomic Time Series Facts: The Effects of Monetary Policy," Cowles Foundation Discussion Papers 1011, Cowles Foundation for Research in Economics, Yale University.
    11. Kevin Hoover & Selva Demiralp, 2003. "Searching for the Causal Structure of a Vector Autoregression," Working Papers 33, University of California, Davis, Department of Economics.
    12. Selva Demiralp & Kevin D. Hoover & Stephen J. Perez, 2008. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 509-533, 08.
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    20. repec:oup:restud:v:61:y:1994:i:1:p:19-30 is not listed on IDEAS
    21. Alessio Moneta, 2008. "Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis," Empirical Economics, Springer, vol. 35(2), pages 275-300, September.
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