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| AN INTRODUCTION TO TIME SERIES ANALYSIS AND FORECASTING
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With Applications of SAS® and SPSS®
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By
Robert Yaffee, New York University, New York, U.S.A.
Monnie McGee, Hunter College, City University of New York
Description
Providing a clear explanation of the fundamental theory of time series analysis and forecasting, this book couples theory with applications
of two popular statistical packages--SAS and SPSS. The text examines moving average, exponential smoothing, Census X-11 deseasonalization,
ARIMA, intervention, transfer function, and autoregressive error models and has brief discussions of ARCH and GARCH models. The book
features treatments of forecast improvement with regression and autoregression combination models and model and forecast evaluation,
along with a sample size analysis for common time series models to attain adequate statistical power. To enhance the book's value as
a teaching tool, the data sets and programs used in the book are made available on the Academic Press Web site. The careful linkage of
the theoretical constructs with the practical considerations involved in utilizing the statistical packages makes it easy for the user
to properly apply these techniques.
Audience
Upper level undergraduate and graduate students, professors, and researchers studying: time series analysis and forecasting; longitudinal
quantitative analysis; and quantitative policy analysis. Students, professors and researchers in the social sciences, business, management,
operations research, engineering, and applied mathematics.
Contents
Preface.
Introduction and Overview:
Purpose.
Time Series.
Missing Data.
Sample Size.
Representativeness.
Scope of Application.
Stochastic and Deterministic Processes.
Stationarity.
Methodological Approaches.
Importance.
Notation.
Extrapolative and Decomposition
Models:
Introduction.
Goodness-of-Fit Indicators.
Average Techniques.
Exponential Smoothing.
Decomposition Methods.
New Features
of Census X-12.
Introduction of Box-Jenkins Time Series Analysis:
Introduction.
The importance of Time Series Analysis
Modeling.
Limitations.
Assumptions.
Time Series.
Tests for Nonstationarity.
Stabilizing the Variance.
Structural or Regime Stability.
Strict Stationarity.
Implications of Stationarity.
The Basic ARIMA Model:
Introduction to ARIMA.
Graphical Analysis
of Time Series Data.
Basic Formulation of the Autoregressive Integrated Moving Average Model.
The Sample Autocorrelation Function.
The
Standard Error of the ACF.
The Bounds of Stationarity and Invertibility.
The Sample Partial Autocorrelation Function.
Bounds of Stationarity
and Invertibility Reviewed.
Other Sample Autocorrelation Funcations.
Tentative Identification of Characteristic Patterns of Integrated,
Autoregressive, Moving Average, and ARMA Processes.
Seasonal ARIMA Models:
Cyclicity.
Seasonal Nonstationarity.
Seasonal
Differencing.
Multiplicative Seasonal Models.
The Autocorrelation Structure of Seasonal ARIMA Models.
Stationarity and Invertibility
of Seasonal ARIMA Model.
A Modeling Strategy for the Seasonal ARIMA Model.
Programming Seasonal Multiplicative Box-Jenkins Models.
Alternative
Methods of Modeling Seasonality.
The Question of Deterministic or Stochastic Seasonality.
Estimation and Diagnosis:
Introduction.
Estimation.
Diagnosis of the Model.
Metadiagnosis and Forecasting:
Introduction.
Metadiagnosis.
Forecasting
with Box-Jenkins Models.
Characteristics of the Optimal Forecast.
Basic Combination of Forecast.
Forecast Evaluation.
Statistical Package
Forecast Syntax.
Regression Combination of Forecasts.
Intervention Analysis:
Introduction: Event Interventions and Their
Impacts.
Assumptions of the Event Intervention (Impact Model).
Impact Analysis Theory.
Significance Tests for Impulse Response Functions.
Modeling Strategies for Impact Analysis.
Programming Impact Analysis.
Applications of Impact Analysis.
Advantages of Intervention Analysis.
Limitations of Intervention Analysis.
Transfer Function Models:
Definition of a Transfer Function.
Importance.
Theory
of the Transfer Function Model.
Modeling Strategies.
Cointegration.
Long-Run and Short-Run Effects in Dynamic Regression.
Basic Characteristics
of a Good Time Series Model.
Chapter 10: Autoregressive Error Models:
The Nature of Serial Correlation of Error.
Sources
of Autoregressive Error.
Autoregressive Models with Serially Correlated Errors.
Tests for Serial Correlation of Error.
Corrective Algorithms
for Regression Models with Autocorrelated Error.
Forecasting with Autocorrelated Error Models.
Programming Regression with Autocorrelated
Errors.
Autoregression in Combining Forecasts.
Models with Stochastic Variance.
A Review of Model and Forecast Evaluation:
Model and Forecat Evaluation.
Model Evaluation.
Comparative Forecast Evaluation.
Comparison of Individual Forecast Methods.
Comparison
of Combined Forecast Models.
Power Analysis and Sample Size Determination for Well-Known Time Series Models:
Census
X-11.
Box-Jenkins Models.
Tests for Nonstationarity.
Intervention Analysis and Transfer Functions.
Regression with Autoregressive Errors.
Conclusion.
Chapter References.
Appendix A.
Glossary.
Index.
| Bibliographic details |
Hardbound, 528 pages, publication date: APR-2000
ISBN-13: 978-0-12-767870-2
ISBN-10: 0-12-767870-0
Imprint: ACADEMIC PRESS
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Last update: 22 Sep 2009
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