Extracting the Cyclical Component in Hours Worked
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Mauro Bernardi
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.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Semi-Parametric Forecasting of Realized Volatility
- Debt Sustainability in Selected Euro Area Countries: Empirical Evidence Estimating Time-Varying Parameters
- Purchasing Power Parity Analyzed from a Continuous-Time Model
- International Output Convergence, Breaks, and Asymmetric Adjustment
- Extracting the Cyclical Component in Hours Worked
- Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions
Artikel in diesem Heft
- Article
- Semi-Parametric Forecasting of Realized Volatility
- Debt Sustainability in Selected Euro Area Countries: Empirical Evidence Estimating Time-Varying Parameters
- Purchasing Power Parity Analyzed from a Continuous-Time Model
- International Output Convergence, Breaks, and Asymmetric Adjustment
- Extracting the Cyclical Component in Hours Worked
- Volatility Feedback and Risk Premium in GARCH Models with Generalized Hyperbolic Distributions
