Wayne A. Fuller

Introduction to Statistical Time Series (eBook, PDF)

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: For Dummies
• Erscheinungstermin: 25. September 2009
• Englisch
• ISBN-13: 9780470317754
• Artikelnr.: 38209332

Autorenporträt

WAYNE A. FULLER is Distinguished Professor in the Departments of Statistics and Economics at Iowa State University. He is the author of Measurement Error Models and numerous articles in time series, survey sampling, and econometrics. A Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the Econometric Society, he received his PhD in agricultural economics from Iowa State University.

Inhaltsangabe

Moving Average and Autoregressive Processes.
Introduction to Fourier Analysis.
Spectral Theory and Filtering.
Some Large Sample Theory.
Estimation of the Mean and Autocorrelations.
The Periodogram, Estimated Spectrum.
Parameter Estimation.
Regression, Trend, and Seasonality.
Unit Root and Explosive Time Series.
Bibliography.
Index.