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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 (R) 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.
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 (R) 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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