EBOOK Time Series Analysis by State Space Methods:Second Edition

This new edition updates Durbin & Koopman's important text on the        state space approach to time series analysis. The distinguishing feature of state        space time series models is that observations are regarded as made up of distinct        components such as trend, seasonal, regression elements and disturbance terms, each        of which is modelled separately. The techniques that emerge from this approach are        very flexible and are capable of handling a much wider range of problems than        themain analytical system currently in use for time series analysis, the Box-Jenkins        ARIMA system. Additions to this second edition include the filtering of nonlinear        and non-Gaussian series.Part I of the book obtains the mean and variance of the        state, of a variable intended to measure the effect of an interaction and of        regression coefficients, in terms of the observations.Part II extends the treatment        to nonlinear and non-normal models. For these, analytical solutions are not        available so methods are based on simulation.