State-space methods for time series analysis : theory, applications and software / Casals, Jose...[et al.]
Material type:
TextSeries: Monographs on statistics and applied probability ; 149.Publication details: Boca Raton : CRC Press, ©2016.Description: xxvii, 269 pages : illustrations ; 24 cmISBN: - 9781482219593
- 000SA.3 23 C334
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 000SA.3 C334 (Browse shelf(Opens below)) | Available | 137812 |
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| 000SA.3 B482 Long-memory processes : | 000SA.3 B857 Time series | 000SA.3 B864 Introduction to time series and forecasting / | 000SA.3 C334 State-space methods for time series analysis : | 000SA.3 D263 Handbook of discrete-valued time series / | 000SA.3 D319 Elements of nonlinear time series analysis and forecasting / | 000SA.3 D438 Basic data analysis for time series with R / |
Includes bibliographical references and indexes.
1. Introduction --
2. Linear state-space models --
3. Model transformations --
4. Filtering and smoothing --
5. Likelihood computation for fixed-coefficients models --
6. The likelihood of models with varying parameters --
7. Subspace methods --
8. Signal extraction --
9. The VARMAX representation of a state-space model --
10. Aggregation and disaggregation of time series --
11. Cross-sectional extension : longitudinal and panel data --
Appendices.
This book 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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