Multivariate Time Series With Linear State Space Structure

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.