The book titled "Multivariate Time Series With Linear State Space
Structure" written by Victor Gomez is available both at Amazon and
http://www.springer.com/gp/book/9783319285986
This book presents a comprehensive study of multivariate time
series with linear state space structure. The emphasis is put on
both the clarity of the theoretical concepts and on efficient
algorithms for implementing the theory. In particular, it
investigates the relationship between VARMA and state space models,
including canonical forms. It also highlights the relationship
between Wiener-Kolmogorov and Kalman filtering both with an
infinite and a finite sample. The strength of the book also lies in
the numerous algorithms included for state space models that take
advantage of the recursive nature of the models. Many of these
algorithms can be made robust, fast, reliable and efficient. The
book is accompanied by a MATLAB package called SSMMATLAB and a
webpage presenting implemented algorithms with many examples and
case studies. Though it lays a solid theoretical foundation, the
book also focuses on practical application, and includes exercises
in each chapter. It is intended for researchers and students
working with linear state space models, and who are familiar with
linear algebra and possess some knowledge of statistics.