Description

The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter.

Major topics include:
* Moving average and autoregressive processes
* Introduction to Fourier analysis
* Spectral theory and filtering
* Large sample theory
* Estimation of the mean and autocorrelations
* Estimation of the spectrum
* Parameter estimation
* Regression, trend, and seasonality
* Unit root and explosive time series

To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

Introduction to Statistical Time Series

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£168.95

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Hardback by Wayne A. Fuller

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Short Description:

The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of... Read more

    Publisher: John Wiley & Sons Inc
    Publication Date: 04/04/1996
    ISBN13: 9780471552390, 978-0471552390
    ISBN10: 0471552399

    Number of Pages: 728

    Non Fiction , Mathematics & Science , Education

    Description

    The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter.

    Major topics include:
    * Moving average and autoregressive processes
    * Introduction to Fourier analysis
    * Spectral theory and filtering
    * Large sample theory
    * Estimation of the mean and autocorrelations
    * Estimation of the spectrum
    * Parameter estimation
    * Regression, trend, and seasonality
    * Unit root and explosive time series

    To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

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