Time Series: State Space Methods

2nd Edition
Koopman, S.J.; Commandeur, J.J.F.

State space modeling provides a unified methodology for treating a wide range of problems in time series analysis. The Kalman filter and its related methods have become key tools in the analysis of time series in economics, finance, and in many other fields as well. In an increasingly more complex world, static and dynamic models have proven to be too limited in empirical and relevant policy studies. The modeling of time-varying features in a time series has been given much attention recently. In this article we review and provide some adequate details and guidance for the adaptation of state space methods in univariate and multivariate time series analysis. We provide more detailed discussions for linear Gaussian model formulations and more concise reviews for nonlinear and non-Gaussian departures.

Published in
International Encyclopedia of the Social and Behavioral Sciences
Wright, J.

SWOV publication

This is a publication by SWOV, or that SWOV has contributed to.