TY  - BOOK
AU  - Casals,Jose
AU  - Garcia-Hiernaux,Alfredo
AU  - Jerez,Miguel
AU  - Sotoca,Sonia
AU  - Trindade,A.Alexandre
TI  - State-space methods for time series analysis: theory, applications and software 
T2  - Monographs on statistics and applied probability
SN  - 9781482219593
U1  - 000SA.3 23
PY  - 2016///
CY  - Boca Raton : 
PB  - CRC Press, 
KW  - Time-series analysis
KW  - State-space methods
N1  - 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
N2  - 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
ER  -