State-Space Methods for Time Series Analysis

Released on by Jose Casals
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  • State-Space Methods for Time Series Analysis Book Detail

  • Author : Jose Casals
  • Release Date : 2018-09-03
  • Publisher : CRC Press
  • Genre : Mathematics
  • Pages : 286
  • ISBN 13 : 131536025X
  • File Size : 7,7 MB
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State-Space Methods for Time Series Analysis by Jose Casals PDF Summary

Book Description: The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

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Time Series Analysis by State Space Methods

Time Series Analysis by State Space Methods

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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 s

Time Series Analysis by State Space Methods

Time Series Analysis by State Space Methods

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State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Eco