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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.

Cleaning up the Errors in the Monthly "Employment Situation" Report: A Multivariate State-Space Approach