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).
