Extracting the Cyclical Component in Hours Worked: a Bayesian Approach

Mauro Bernardi, Giuseppe Della Corte and Tommaso Proietti

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Gaussian Mixtures; Robust signal extraction; State Space Models; Bayesian model selection; Seasonality (search for similar items in EconPapers)
JEL-codes: C11 C22 C52 E32 (search for similar items in EconPapers)
Date: 2008-05
New Economics Papers: this item is included in nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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