Produktbild: State-Space Methods for Time Series Analysis
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State-Space Methods for Time Series Analysis Theory, Applications and Software

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.06.2020

Verlag

Taylor & Francis

Seitenzahl

298

Maße (L/B/H)

23,4/15,6/1,6 cm

Gewicht

456 g

Farbe

Anthrazit / Weiß

Sprache

Englisch

ISBN

978-0-367-57058-3

Beschreibung

Rezension

"The way the authors of describe their book, it is the fruit of a long-lasting love affair with state space models, which started in the 1980s, inspired by the work of Box and Jenkins. Judging from the density of equations and symbols, it must be the theory of the subject that attracts them most. ... This book is not for the fainthearted. It explains a lotabout state space models. To use them, you have to accept the philosophy of detailed modelling of time series. In summary, if you are a specialist, or want to become one, you will like this book."
- Paul Eilers, ISCB News, May 2017

"This book synthesizes and presents the computational advantages of the state-space approach over the traditional time domain approaches to linear time series analysis. The explicit connection between the mainstream ARIMA time series models and the state-space representation, one of the main features of the book, is achieved by presenting many examples and procedures to combine, decompose, aggregate, and disaggregate an economic time series into the state-space form. More specifically, it provides a bridge for going back and forth between state-space models and the broad class of VARMAX models...Overall, this is a useful book on sate-space methods for time series analysis and covers substantial amount of material lucidly with a focus on computational aspects and software. It is an excellent reference book for self-study and can also be used as a companion for teaching time series analysis along with a standard time series text."
-Mohsen Pourahmadi, Texas A&M University, in the Journal of Time Series Analysis, June 2017

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

30.06.2020

Verlag

Taylor & Francis

Seitenzahl

298

Maße (L/B/H)

23,4/15,6/1,6 cm

Gewicht

456 g

Farbe

Anthrazit / Weiß

Sprache

Englisch

ISBN

978-0-367-57058-3

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: State-Space Methods for Time Series Analysis
  • 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

    Appendices
    Appendix A: Some results in numerical algebra and linear systems
    Appendix B: Asymptotic properties of maximum likelihood estimates
    Appendix C: Software (E4)
    Appendix D: Downloading E4 and the examples in this book

    Bibliography