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Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?

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Author Info
James C. Morley (Washington University in St. Louis)
Charles R. Nelson (University of Washington)
Eric Zivot (University of Washington)
Abstract

This paper reconciles two widely used decompositions of GDP into trend and cycle that yield starkly different results. The Beveridge-Nelson (BN) decomposition implies that a stochastic trend accounts for most of the variation in output, whereas the unobserved-components (UC) implies cyclical variation is dominant. Which is correct has broad implications for the relative importance of real versus nominal shocks. We show the difference arises from the restriction imposed in UC that trend and cycle innovations are uncorrelated. When this restriction is relaxed, the UC decomposition is identical to the BN decomposition. Furthermore, the zero-correlation restriction can be rejected for U.S. quarterly GDP, with the estimated correlation being -0.9. Copyright (c) 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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Publisher Info
Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 85 (2003)
Issue (Month): 2 (03)
Pages: 235-243
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Handle: RePEc:tpr:restat:v:85:y:2003:i:2:p:235-243

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  1. de Silva, Ashton, 2007. "A multivariate innovations state space Beveridge Nelson decomposition," MPRA Paper 5431, University Library of Munich, Germany. [Downloadable!]
  2. James Morley & Tara M. Sinclair, 2005. "Testing for Stationarity and Cointegration in an Unobserved Components Framework," Computing in Economics and Finance 2005 451, Society for Computational Economics. [Downloadable!]
  3. Tatsuma Wada & Pierre Perron, 2005. "Trend and Cycles: A New Approach and Explanations of Some Old Puzzles," Computing in Economics and Finance 2005 252, Society for Computational Economics. [Downloadable!]
  4. Arabinda Basistha & Richard Startz, 2005. "Measuring the NAIRU with Reduced Uncertainty: A Multiple Indicator-Common Component Approach," Computing in Economics and Finance 2005 46, Society for Computational Economics. [Downloadable!]
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  5. N. Kundan Kishor & Evan F. Koenig, 2005. "VAR estimation and forecasting when data are subject to revision," Working Papers 05-01, Federal Reserve Bank of Dallas. [Downloadable!]
  6. Jaromír Beneš & David Vávra, 2005. "Eigenvalue filtering in VAR models with application to the Czech business cycle," Working Paper Series 549, European Central Bank. [Downloadable!]
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