This paper explores 35 years of the American business cycle with the Hidden Markov Model (HMM) as a monitoring tool using monthly data. It exhibits ten US time series, which offer reliable information to detect recessions in real time. It also assesses the performances of different and complementary “recession models” based on Markovian processes : the “Pooled data model” and a multivariate HMM, and draws two main conclusions: simple HMM are decisive to monitor the business cycle providing that the series are proved highly reliable; models adding a multivariate dimension are useful but work marginally better than a simple summary : the inner quality of series seem to dominate their modeling. This paper introduces a new reading of the business cycle through, a favored recession model and concludes about leading and “real time detection” limitations. This paper is written in French.
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Paper provided by EconWPA in its series Econometrics with number
0407002.
Length: 37 pages Date of creation: 04 Jul 2004 Date of revision:
28 Mar 2005 Handle: RePEc:wpa:wuwpem:0407002
Note: Type of Document - pdf; pages: 37. This paper introduces two new business cycle stochastic indicator of the US economy, with a foolproof recession index. Contact details of provider: Web page: http://129.3.20.41
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Find related papers by JEL classification: C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
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