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- Toujours un magasin près de chez vous
State-Space Methods for Time Series Analysis
Theory, Applications and Software
Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez
190,95 €
+ 381 points
Format
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.
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Spécifications
Parties prenantes
- Auteur(s) :
- Editeur:
Contenu
- Nombre de pages :
- 298
- Langue:
- Anglais
- Collection :
Caractéristiques
- EAN:
- 9781482219593
- Date de parution :
- 23-03-16
- Format:
- Livre relié
- Format numérique:
- Genaaid
- Dimensions :
- 157 mm x 234 mm
- Poids :
- 539 g
