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Extracting the Cyclical Component in Hours Worked

Author

Listed:
  • Bernardi Mauro

    () (University of Rome Tor Vergata)

  • Della Corte Giuseppe

    () (Bank of Italy, Rome)

  • Proietti Tommaso

    () (University of Rome Tor Vergata)

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 finally show that if a flexible seasonal model is adopted for the unadjusted series, the level of outlier contamination is drastically reduced.

Suggested Citation

  • Bernardi Mauro & Della Corte Giuseppe & Proietti Tommaso, 2011. "Extracting the Cyclical Component in Hours Worked," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(3), pages 1-28, May.
  • Handle: RePEc:bpj:sndecm:v:15:y:2011:i:3:n:5
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    References listed on IDEAS

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    Cited by:

    1. Marczak, Martyna & Proietti, Tommaso & Grassi, Stefano, 2018. "A data-cleaning augmented Kalman filter for robust estimation of state space models," Econometrics and Statistics, Elsevier, vol. 5(C), pages 107-123.

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