Synopses & Reviews
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!"
-Journal of the Royal Statistical Society
"A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates."
-Choice
Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.
Synopsis
Econometric Analysis by Control Methods Gregory C. Chow Reports on new developments in the techniques and applications of stochastic control in economics that have token place since the authors Analysis and Control of Dynamic Economic Systems (Wiley, 1975). Includes techniques tailored to nonlinear, simultaneous-equation models in economics, and a guide to a computer program for finding optimal control solutions; control techniques for the analysis and formulation of economic policies and the comparison of econometric models; estimation and control of econometric models under the assumption of rational expectations; and the application of stochastic control methods to models in continuous time. 1981 320 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that: aid in the systematic location of data points that are unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data; help to identify the variables involved in each; pinpoint estimated coefficients that are potentially the most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action. Wiley Series in Probability and Mathematical Statistics. 1980 292 pp. Forecasting with Univariate Box-Jenkins Models Concepts and Cases Alan Pankratz Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using red data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another. 1983 560 pp.
Synopsis
Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical models and methods used to produce short-term forecasts. Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical intermediate-level discussion of a venery of forecasting tools, and explains how they relate to one another both in theory and practice. While the emphasis is on the familiar regression models, and exponential smoothing and parametric time series models for nonseasonal and seasonal data, the text also treats a number of special topics such as transfer function analysis, Kalman filtering, state space models, Bayesian forecasting, seasonal adjustment and forecast evaluation. A unique feature of the presentation is the interrelation of forecasts from exponential smoothing and forecasts from ARIMA (autoregressive integrated moving average) time series models. This discussion shows which ARIMA models imply the various exponential smoothing forecast procedures as special cases. The text also adopts a model-based approach to forecasting, one which uses available data to construct appropriate models. Statistical Methods for Forecasting serves as an outstanding textbook for graduate and advanced undergraduate courses in forecasting for students of statistics, mathematics, business, engineering, and the social sciences, as well as a basic working reference for professional forecasters in business, industry, and government. It includes a large number of examples and exercises (using real data) and provides numerous time series, autocorrelation and partial autocorrelation plots as illustrations.
About the Author
About the authors Bovas Abraham is Associate Professor in the Department of Statistics and Actuarial Science, at the University of Waterloo, Ontario, Canada. He is a member of the American Statistical Association, the American Society for Duality Control, the Canadian Statistical Association and a Fellow of the Royal Statistical Society. Dr. Abraham received his Ph.D. in statistics from the University of Wisconsin, Madison. Johannes Ledolter is an Associate Professor in bath the Deportment of Statistics and Actuarial Science, and the Department of Management Sciences at the University of Iowa. He is a member of the American Statistical Association and a Fellow of the Royal Statistical Society. Dr. Ledolter is also coauthor of Forecasting Using Leading Indicators. He received his Ph.D. in statistics from the University of Wisconsin, Madison.
Table of Contents
Introduction and Summary.
The Regression Model and Its Application In Forecasting.
Regression and Exponential Smoothing Methods to Forecast Non-seasonal Time Series.
Regression and Exponential Smoothing Methods to Forecast Seasonal Time Series.
Stochastic Time Series Models.
Seasonal Autoregressive Integrated Moving Average Models.
Relationships Between Forecasts from General Exponential Smoothing and Forecasts from Arima Time Series Models.
Special Topics.
References.
Exercises.
Data Appendix.
Table Appendix.
Index.