Abstract: This paper examines the underlying state of the labor market,
assuming data in the monthly "Employment Situation" are
contaminated
by measurement error and other transient noise. To better filter out
unobserved noise, the methodology exploits correlations among
labor-market series. Household employment and labor force have
cross-correlated sampling errors; establishment employment and
hours-worked may, also. The Kalman filtering procedure also exploits
fundamental economic relationships among these series. Error
cross-correlations and economic relationships shape a multivariate
labor-market model where observed variables embody unobserved
components: trend, cycle and noise. Maximum-likelihood estimation
enables construction of labor series from which noise components have
been removed.
Keywords: Signal extraction, kalman filter, employment situation
Full paper (70 KB PDF)
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Last update: March 2, 1998
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