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Extracting the Cyclical Component in Hours Worked: a Bayesian Approach

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  • Bernardi, Mauro
  • Della Corte, Giuseppe
  • Proietti, Tommaso

Abstract

The series on average hours worked in the manufacturing sector is a key leading indicator of the U.S. business cycle. The paper deals with robust estimation of the cyclical component for the seasonally adjusted time series. This is achieved by an unobserved components model featuring an irregular component that is represented by a Gaussian mixture with two components. The mixture aims at capturing the kurtosis which characterizes the data. After presenting a Gibbs sampling scheme, we illustrate that the Gaussian mixture model provides a satisfactory representation of the data, allowing for the robust estimation of the cyclical component of per capita hours worked. Another important piece of evidence is that the outlying observations are not scattered randomly throughout the sample, but have a distinctive seasonal pattern. Therefore, seasonal adjustment plays a role. We ¯nally show that, if a °exible seasonal model is adopted for the unadjusted series, the level of outlier contamination is drastically reduced.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 8967.

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Date of creation: May 2008
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Handle: RePEc:pra:mprapa:8967

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Related research

Keywords: Gaussian Mixtures; Robust signal extraction; State Space Models; Bayesian model selection; Seasonality;

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References

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  1. Glosser, Stuart M. & Golden, Lonnie, 1997. "Average work hours as a leading economic variable in US manufacturing industries," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 175-195, June.
  2. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 2(1), pages 107-160.
  3. Harvey, A.C. & Trimbur, T.M. & van Dijk, H.K., 2005. "Trends and cycles in economic time series: A Bayesian approach," Econometric Institute Research Papers EI 2005-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  5. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, Oxford University Press, number 9780198523543, October.
  6. Giordani, P. & Kohn, R. & van Dijk, D.J.C., 2005. "A unified approach to nonlinearity, structural change and outliers," Econometric Institute Research Papers EI 2005-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  7. Cho, J-O. & Cooley, T.F., 1988. "Employment And Hours Over The Business Cycle," Papers, Rochester, Business - General 88-03, Rochester, Business - General.
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Cited by:
  1. Guglielmo Maria Caporale & Luis A. Gil-Alana, 2012. "Persistence and Cycles in US Hours Worked," CESifo Working Paper Series 3767, CESifo Group Munich.

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