In statistics, the Kalman filter is a mathematical method whose purpose
is to use a series of measurements observed over time, containing random
variations and other inaccuracies, and produce estimates that tend to be
closer to the true unknown values than those that would be based on a
single measurement alone. This Brief offers developments on Kalman
filtering subject to general linear constraints. There are essentially
three types of contributions: new proofs for results already
established; new results within the subject; and applications in
investment analysis and macroeconomics, where the proposed methods are
illustrated and evaluated. The Brief has a short chapter on linear state
space models and the Kalman filter, aiming to make the book
self-contained and to give a quick reference to the reader (notation and
terminology). The prerequisites would be a contact with time series
analysis in the level of Hamilton (1994) or Brockwell & Davis (2002) and
also with linear state models and the Kalman filter - each of these
books has a chapter entirely dedicated to the subject. The book is
intended for graduate students, researchers and practitioners in
statistics (specifically: time series analysis and econometrics).