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A model-based ranking of U.S. recessions


Author Info

  • Ivan Jeliazkov

    (UC Irvine)

  • Rui Liu

    (UC Irvine)


A dynamic factor VAR model, estimated by MCMC simulation, is employed to assess the relative severity of post-war U.S. recessions. Joint modeling and estimation of all model unknowns yields rank estimates that fully account for parameter uncertainty. A convenient by-product of the simulation approach is a probability distribution of possible recession ranks that (i) accommodates uncertainty about the exact location of troughs, and (ii) can be used to resolve any potential inconsistencies or ties in the rank estimates. These features distinguish the approach from single-variable measures of downturns that ignore the co-movement and dynamic dependence and could lead to contradictory conclusions about timing and relative severity.

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Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 30 (2010)
Issue (Month): 3 ()
Pages: 2289-2296

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Handle: RePEc:ebl:ecbull:eb-10-00483

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Related research

Keywords: Bayesian estimation; business cycle; dynamic factor; Markov chain Monte Carlo (MCMC); vector autoregressive (VAR) model;

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Cited by:
  1. Paul Viefers, 2011. "Bayesian Inference for the Mixed-Frequency VAR Model," Discussion Papers of DIW Berlin 1172, DIW Berlin, German Institute for Economic Research.
  2. Chan, Joshua & Eisenstat, Eric, 2012. "Marginal Likelihood Estimation with the Cross-Entropy Method," MPRA Paper 40051, University Library of Munich, Germany.
  3. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.


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