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Business-Cycle Filtering of Macroeconomic Data Via A Latent Business-Cycle Index

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Author Info

  • Michael Dueker

    (Federal Reserve Bank of St. Louis)

  • Charles Nelson

Abstract

We use Markov chain Monte Carlo methods to augment, via a novel multimove sampling scheme, a vector autoregressive (VAR) system with a latent business-cycle index that is negative during recessions and positive during expansions. We then sample counterfactual values of the macroeconomic variables in the case where the latent business-cycle index is held constant. These counterfactual values represent posterior beliefs about how the economy would have evolved absent business-cycle fluctuations. One advantage is that a VAR framework provides model-consistent counterfactual values in the same way that VARs provide model-consistent forecasts, so data series are not filtered in isolation from each other. We apply these methods to estimate the business-cycle components of industrial production, consumer price inflation, the federal funds rate, and the spread between long-term and short-term interest rates. These decompositions provide an explicitly counterfactual approach to isolating the effects of the business cycle and to deriving empirical business-cycle facts.

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Bibliographic Info

Paper provided by University of Washington, Department of Economics in its series Working Papers with number UWEC-2006-13-P.

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Date of creation: Nov 2006
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Publication status: Published in Macroeconomics Dynamics, Volume Vol. 10, pp. 1-22.
Handle: RePEc:udb:wpaper:uwec-2006-13-p

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Web page: http://www.econ.washington.edu/
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Cited by:
  1. Michael D. Bordo & Michael J. Dueker & David C. Wheelock, 2009. "Inflation, monetary policy and stock market conditions: quantitative evidence from a hybrid latent-variable VAR," Working Papers 2008-012, Federal Reserve Bank of St. Louis.
  2. Michael D. Bordo & Michael J. Dueker & David C. Wheelock, 2008. "Inflation, Monetary Policy and Stock Market Conditions," NBER Working Papers 14019, National Bureau of Economic Research, Inc.
  3. Tatsuma Wada & Pierre Perron, 2006. "State Space Model with Mixtures of Normals: Specifications and Applications to International Data," Boston University - Department of Economics - Working Papers Series WP2006-029, Boston University - Department of Economics.

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