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Introduction to Statistical Time Series, by Wayne A. Fuller
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The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, 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 sample statistics, and difference equations, has been included.
- Sales Rank: #2533904 in Books
- Published on: 1995-12
- Original language: English
- Number of items: 1
- Dimensions: 9.57" h x 1.53" w x 6.38" l, 2.51 pounds
- Binding: Hardcover
- 728 pages
From the Back Cover
The subject of time series is of considerable interest, especially among researchers in econometrics, engineering, and the natural sciences. As part of the prestigious Wiley Series in Probability and Statistics, this book provides a lucid introduction to the field and, in this new Second Edition, covers the important advances of recent years, including nonstationary models, nonlinear estimation, multivariate models, state space representations, and empirical model identification. New sections have also been added on the Wold decomposition, partial autocorrelation, long memory processes, 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 sample statistics, and difference equations, has been included.
About the Author
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.
Most helpful customer reviews
29 of 29 people found the following review helpful.
excellent graduate text covering both time and frequency domain methods
By Michael R. Chernick
In 1981 I used this book for a graduate seminar that I taught on time series analysis at UC Santa Barbara. The course was successful because I followed the text closely. It presents the material in a rigorous and cogent manner. At the time there were a few competing books but some emphasized time domain methods and others were strictly frequency domain approaches. Fuller balances the two very well and his book is better written and organized than most of the competitors at that time (1981).
Today there are a lot more books to choose from. You can check my listmania list on time series books to get an idea. I particularly like Brockwell and Davis' book as a competitor to Fuller for a graduate level time series seminar.
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