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

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.