Abstract
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.
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