State-space methods for time series analysis : theory, applications and software /: theory, applications and software. (2018)

• Record Type: Book
• Further Information: Note: Jose Casals, Alfredo Garcia-Hiernaux, Miguel Jerez, Sonia Sotoca, A. Alexandre Trindade.
• Authors: (Banker), Casals, Jose; Garcia-Hiernaux, Alfredo; Jerez, Miguel; Sotoca, Sonia; Trindade, A. Alexandre
• Contents: Introduction Linear state-space models ; The multiple error model; Single error models Model transformations ; Model decomposition; Model combination; Change of variables in the output; Uses of these transformations Filtering and smoothing ; The conditional moments of a state-space model; The Kalman filter; Decomposition of the smoothed moments; Smoothing for a general state-space model; Smoothing for fixed-coefficients and single-error models; Uncertainty of the smoothed estimates in a fixed-coefficients SEM; Examples Likelihood computation for fixed-coefficients models ; Maximum likelihood estimation; The likelihood for a non-stationary model; The likelihood for a model with inputs; Examples The likelihood of models with varying parameters ; Regression with time-varying parameters; Periodic models; The likelihood of models with GARCH errors; Examples Subspace methods ; Theoretical foundations; System order estimation; Constrained estimation; Multiplicative seasonal models; Examples Signal extraction ; Input and error-related components; Estimation of the deterministic components; Decomposition of the stochastic component; Structure of the method; Examples The VARMAX representation of a state-space model ; Notation and previous results; Obtaining the VARMAX form of a state-space model; Practical applications and examples Aggregation and disaggregation of time series ; The effect of aggregation on a state-space model; Observability in the aggregated model; Specification of the high-frequency model; Empirical example The cross-sectional extension: longitudinal and panel data ; Model formulation; The Kalman filter; The linear mixed model in state-space form; Maximum likelihood estimation; Missing data modifications; Real data examples Appendix A: Some results in numerical algebra and linear systems; Appendix B: Asymptotic properties of maximum likelihood estimates; Appendix C: Software (E 4 ); Appendix D: Downloading E 4 and the examples in this book Bibliography
• Publisher Details: Place of publication not identified : Chapman and Hall/CRC
• Publication Date: 2018
• Extent: 1 online resource (270 pages), (25 illustrations)
• Subjects: 519.55; State-space methods; Time-series analysis
• Languages: English
• ISBNs: 9781315360256
• Related ISBNs: 131536025X
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.