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Biometrika 1983 70(1):275-278; doi:10.1093/biomet/70.1.275
© 1983 by Biometrika Trust
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MISCELLANEA

Exact likelihood of vector autoregressive-moving average process with missing or aggregated data

C. F. ANSLEY and R. KOHN

Graduate School of Business, University of Chicago Chicago, Illinois, U.S.A.

This note points out that by using the Kalman filter with nonconstant coefficients, we can compute the exact likelihood of an autoregressive-moving average process observed with noise, when some of our observations are either missing or aggregated.

Key Words: Aggregated data • Autoregressive-moving average model • Exact likelihood • Kalman filter • Missing data


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