Introduction to Statistical Time Series.

Wayne A. Fuller .

ISBN 10: 0471287156 / ISBN 13: 9780471287155

Published by John Wiley & Sons, New York, NY, 1976

About this Item

Near Fine in Wraps: shows only very minor indications of use: just a hint of wear to extremities, though the lower front corner tip is very mildly bumped; a thin, faint line of soiling at the top edge and a couple of faint smudges at the fore-edge. The Binding is square and secure; the text is clean. Very close to 'As New'. NOT a Remainder, Book-Club, or Ex-Library. 8vo. 470pp. Wiley Series in (Probability & Mathematical Statistics). First Edition [1971]; Second Printing. Hardback: Lacks DJ. 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. Fuller balances time domain methods and frequency domain approaches very well and his book is better written and organized than most.

Seller Inventory # 44224

Bibliographic Details

• Title: Introduction to Statistical Time Series.
• Publisher: John Wiley & Sons, New York, NY
• Publication Date: 1976
• Language: English
• ISBN 10: 0471287156
• ISBN 13: 9780471287155
• Binding: Hardcover
• Edition: First Edition [1971]; Second Printing.
• Dust Jacket Condition: Dust Jacket Included
• Seller catalogs:
  • Mathematics

About this title

Synopsis

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.