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Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions

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  • Lawrence J. Christiano
  • Martin Eichenbaum
  • Robert Vigfusson

Abstract

We show that the standard procedure for estimating long-run identified vector autoregressions uses a particular estimator of the zero-frequency spectral density matrix of the data. We develop alternatives to the standard procedure and evaluate the properties of these alternative procedures using Monte Carlo experiments in which data are generated from estimated real business cycle models. We focus on the properties of estimated impulse response functions. In our examples, the alternative procedures have better small sample properties than the standard procedure, with smaller bias, smaller mean square error, and better coverage rates for estimated confidence intervals. (JEL: E24, E32, O3) (c) 2006 by the European Economic Association.

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Bibliographic Info

Article provided by MIT Press in its journal Journal of the European Economic Association.

Volume (Year): 4 (2006)
Issue (Month): 2-3 (04-05)
Pages: 475-483

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Handle: RePEc:tpr:jeurec:v:4:y:2006:i:2-3:p:475-483

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  1. Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2006. "Assessing structural VARs," International Finance Discussion Papers 866, Board of Governors of the Federal Reserve System (U.S.).
    • Lawrence J. Christiano & Martin Eichenbaum & Robert Vigfusson, 2007. "Assessing Structural VARs," NBER Chapters, in: NBER Macroeconomics Annual 2006, Volume 21, pages 1-106 National Bureau of Economic Research, Inc.
  2. Andrews, Donald W K & Monahan, J Christopher, 1992. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Econometrica, Econometric Society, vol. 60(4), pages 953-66, July.
  3. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2005. "A critique of structural VARs using real business cycle theory," Working Papers 631, Federal Reserve Bank of Minneapolis.
  4. Jordi Gali, 1999. "Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?," American Economic Review, American Economic Association, vol. 89(1), pages 249-271, March.
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Cited by:
  1. Bekaert, Geert & Hoerova, Marie & Lo Duca, Marco, 2010. "Risk, Uncertainty and Monetary Policy," CEPR Discussion Papers 8154, C.E.P.R. Discussion Papers.
  2. Mertens, Elmar, 2012. "Are spectral estimators useful for long-run restrictions in SVARs?," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1831-1844.
  3. John W. Keating, 2013. "Interpreting Permanent Shocks to Output When Aggregate Demand May Not Be Neutral in the Long Run," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(4), pages 747-756, 06.
  4. Holly, S. & Petrella, I., 2008. "Factor demand linkages and the business cycle: Interpreting aggregate fluctuations as sectoral fluctuations," Cambridge Working Papers in Economics 0827, Faculty of Economics, University of Cambridge.
  5. Dupor, Bill & Han, Jing & Tsai, Yi-Chan, 2009. "What do technology shocks tell us about the New Keynesian paradigm?," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 560-569, May.

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